Search

A novel iterative combinatorial nanoscale search based on the application of cross-sectional synchrotron X-ray nanodiffraction and cross-sectional nanoindentation is used to refine the relationship between deposition conditions, microstructure and properties of nanostructured TiAlN thin films. Using three iterative steps, a nanolamellar TiAlN thin film with a maximal hardness of ∼36 GPa is developed., Because of the tremendous variability of crystallite sizes and shapes in nano­materials, it is challenging to assess the corresponding size–property relationships and to identify microstructures with particular physical properties or even optimized functions. This task is especially difficult for nanomaterials formed by self-organization, where the spontaneous evolution of microstructure and properties is coupled. In this work, two compositionally graded TiAlN films were (i) grown using chemical vapour deposition by applying a varying ratio of reacting gases and (ii) subsequently analysed using cross-sectional synchrotron X-ray nanodiffraction, electron microscopy and nanoindentation in order to evaluate the microstructure and hardness depth gradients. The results indicate the formation of self-organized hexagonal–cubic and cubic–cubic nanolamellae with varying compositions and thicknesses in the range of ∼3–15 nm across the film thicknesses, depending on the actual composition of the reactive gas mixtures. On the basis of the occurrence of the nanolamellae and their correlation with the local film hardness, progressively narrower ranges of the composition and hardness were refined in three steps. The third film was produced using an AlCl3/TiCl4 precursor ratio of ∼1.9, resulting in the formation of an optimized lamellar microstructure with ∼1.3 nm thick cubic Ti(Al)N and ∼12 nm thick cubic Al(Ti)N nanolamellae which exhibits a maximal hardness of ∼36 GPa and an indentation modulus of ∼522 GPa. The presented approach of an iterative nanoscale search based on the application of cross-sectional synchrotron X-ray nanodiffraction and cross-sectional nanoindentation allows one to refine the relationship between (i) varying deposition conditions, (ii) gradients of microstructure and (iii) gradients of mechanical properties in nanostructured materials prepared as thin films. This is done in a combinatorial way in order to screen a wide range of deposition conditions, while identifying those that result in the formation of a particular microstructure with optimized functional attributes.

BackgroundHealth promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. ObjectiveThis study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. MethodsWe developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher’s tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). ResultsAn illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. ConclusionsResults suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

This paper addresses the distributed limited-buffer flowshop scheduling problem with makespan minimization criterion for the first time. The manufacturing layout consists of multiple homogeneous factories and each one is set as a permutation flowshop with limited buffers between any two adjacent machines. Compared with traditional single shop scheduling, this problem is more realistic because of the distribution and limited buffer constraints. Given that the NP-hardness, this paper focuses on the approximate approaches, and successively proposes two constructive heuristics for generating fast schedule and providing good initialization of meta-heuristics, a differential evolution algorithm run in continuous search space to further better the so-obtained schedules, and a discrete differential evolution algorithm implemented directly in combinatorial search space. Extensive experimental results validate that the performance of discrete differential evolution perform the best and other algorithms proposed in this paper are all with satisfactory evaluations.

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP., Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 16 pages, 6 figures

Aim. This paper is the continuation of [1] that proposes using the R programming language for fault tree analysis (FTA). In [1], three examples are examined: fault tree (FT) calculation per known probabilities, dynamic FT calculation per known distributions of times to failure for a system’selements. In the latter example, FTA is performed for systems with elements that are described by different functional and service models. Fault tree analysis (FTA) is one of the primary methods of dependability analysis of complex technical systems. This process often utilizes commercial software tools like Saphire, Risk Spectrum, PTC Windchill Quality, Arbitr, etc. Practically each software tool allows calculating the dependability of complex systems subject to possible common cause failures (CCF). CCF are the associated failures of a group of several elements that occur simultaneously or within a short time interval (i.e. almost simultaneously) due to one common cause (e.g. a sudden change in the climatic service conditions, flooding of the premises, etc.). An associated failure is a multiple failure of several system elements, of which the probability cannot be expressed simply as the product of the probabilities of unconditional failures of individual elements. There are several generally accepted models used in CCF probability calculation: the Greek letters model, the alpha, beta factor models, as well as their variations. The beta factor model is the most simple in terms of associated failures simulation and further dependability calculation. The other models involve combinatorial search associated events in a group of n events, that becomes labor-consuming if the number n is large. Therefore, in the above software tools there are some restrictions on the n, beyond which the probability of CCF is calculated approximately. In the current R FaultTree package version there are no above CCF models, therefore all associated failures have to be simulated manually, which is not complicated if the number of associated events is small, as well as useful in terms of understanding the various CCF models. In this paper, for the selected diagram a detailed analysis of the procedure of associated failures simulation is performed for alpha and beta factor models. The Purposeof this paper consists in the detailed analysis of the alpha and beta factor methods for a certain diagram, in the demonstration of fault tree creation procedure taking account of ССF using R’s FaultTree package. Methods. R’s FaultTree scripts were used for the calculations and FTA capabilities demonstration.Conclusions. Two examples are examined in detail. In the first example, for the selected block diagram that contains two groups of elements subject to associated failures, the alpha factor model is applied. In the second example, the beta factor model is applied. The deficiencies of the current version of FaultTree package are identified. Among the main drawbacks we should indicate the absence of some basic logical gates.

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

International audience; Most studies related to parallel and portfolio search for solving combinatorial problems, such as those found in Boolean satisfiability or constraint programming, evaluate search cost in terms of runtime. However, given the complex computing architectures available today and the focus on the environmental impact of computing, there is growing interest in also considering the energy cost associated with solving these problems. In the context of combinatorial problem-solving, a simple approximation of energy cost is the product of the number of machines multiplied by the runtime spent to solve a problem instance. However, the picture is much more complex due to the impact that the distribution of runtimes, even for solving a single specific instance, can have on search cost. In this paper we present an initial, but comprehensive, study on the impact of runtime distribution on the amount of energy required for combinatorial problem solving characterized by two common continuous runtime distributions, namely the Weibull and Pareto distributions. The primary contribution of this paper is to demonstrate that there is an interesting and non-trivial relationship between runtime, parallelisation, and energy cost in combinatorial solving that is worthy of further study.

A new approach is used to generate combinatorial test suites.Cuckoo Search application is investigated for a new type of application and case study.The strategy opens a new approach in Search Based Software Testing (SBST).The strategy is evaluated through different benchmarks and it is able to get comparative results.Application of combinatorial optimization is also investigated in the current paper. ContextSoftware has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations. ObjectiveCombinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using Cuckoo Search concepts. MethodCuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search. ResultsThe efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing. ConclusionResults show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.

retrieval is the most fundamental requirement for any kind of computing application and which requires search operation to be performed from massive databases implemented by various data structures. Searching an element from the list is the fundamental aspects in computing world. Numbers of algorithms are developed for searching an element among which linear search and binary search are the most popular algorithms. In this paper researcher has made efforts to compare these both algorithms to implement on various data structures and to find out the solution to implement binary search on linked linear list. This paper also analyzes both the algorithms at some extent for the applicability and execution efficiency. This paper also analyzes the few data structures to implement these algorithms. At last based on the linear search and binary search algorithms, one algorithm is designed to function on linked linear list.

Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.

In this paper we consider high-dimensional multiclass classification by sparse multinomial logistic regression. We propose first a feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the nonasymptotic bounds for misclassification excess risk of the resulting classifier. We establish also their tightness by deriving the corresponding minimax lower bounds. In particular, we show that there exist two regimes corresponding to small and large number of classes. The bounds can be reduced under the additional low noise condition. To find a penalized maximum likelihood solution with a complexity penalty requires, however, a combinatorial search over all possible models. To design a feature selection procedure computationally feasible for high-dimensional data, we propose multinomial logistic group Lasso and Slope classifiers and show that they also achieve the minimax order.

The fundamental inverse problem in distance geometry is the one of finding positions from inter-point distances. The Discretizable Molecular Distance Geometry Problem (DMDGP) is a subclass of the Distance Geometry Problem (DGP) whose search space can be discretized and represented by a binary tree, which can be explored by a Branch-and-Prune (BP) algorithm. It turns out that this combinatorial search space possesses many interesting symmetry properties that were studied in the last decade. In this paper, we present a new algorithm for this subclass of the DGP, which exploits DMDGP symmetries more effectively than its predecessors. Computational results show that the speedup, with respect to the classic BP algorithm, is considerable for sparse DMDGP instances related to protein conformation.

Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. hl{This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets.} Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic´ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes. Fil: Rojas, Matias Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina

In 1983, a computer search was performed for ovals in a projective plane of order ten. The search was exhaustive and negative, implying that such ovals do not exist. However, no nonexistence certificates were produced by this search, and to the best of our knowledge the search has never been independently verified. In this paper, we rerun the search for ovals in a projective plane of order ten and produce a collection of nonexistence certificates that, when taken together, imply that such ovals do not exist. Our search program uses the cube-and-conquer paradigm from the field of satisfiability (SAT) checking, coupled with a programmatic SAT solver and the nauty symbolic computation library for removing symmetries from the search., Appears in the Proceedings of the 31st International Workshop on Combinatorial Algorithms (IWOCA 2020)

The practices by which actions are accomplished in interaction are invariably sensitive to context, as it is not only through their composition but also their position that they are recognisable as practices. This means that practices are not readily identifiable through straightforward searches for linguistic units in large datasets of spoken interaction. In this paper, we propose that through an explicitly combinatorial approach to search, the procedural basis of which is grounded in a specific array of compositional and sequential features associated with the candidate practice in question, we can more readily find potentially relevant examples of that phenomenon across large tracts of data, which can then be subjected to rigorous sequential analysis. We argue that this combinatorial search procedure enables us access to data from multiple different sources. We illustrate this combinatorial approach through an analysis of laughter that co-occurs with lapses in the ongoing progressivity of talk-in-interaction in various conversational settings. Careful analysis of post-lapse laughter in a dataset of initial interactions suggests that such laughter often occurs subsequent to silences greater than 500 ms and co-occurs with speaker change. We use these compositional and positional features to construct searches across different spoken corpora held within the Australian National Corpus in order to identify further possible examples of this candidate practice, thereby demonstrating the relevance of this approach for accessing data in conversation analytic research. We suggest that the advantage of an explicitly combinatorial approach to identifying practices, and the actions accomplished through them, is that it enables us to go beyond analysing them in single, isolated datasets, as well as opening up hitherto understudied practices for further investigation.

We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos. The wide diversity of queries coupled with unavailability of annotated activity data limits our ability to train activity models. To bridge the semantic gap we propose to let users describe an activity as a semantic graph with object attributes and inter-object relationships associated with nodes and edges, respectively. We learn node/edge-level visual predictors during training and, at test-time, propose to retrieve activity by identifying likely locations that match the semantic graph. We formulate a novel CRF based probabilistic activity localization objective that accounts for mis-detections, mis-classifications and track-losses, and outputs a likelihood score for a candidate grounded location of the query in the video. We seek groundings that maximize overall precision and recall. To handle the combinatorial search over all high-probability groundings, we propose a highest precision subgraph matching algorithm. Our method outperforms existing retrieval methods on benchmarked datasets., Comment: 1520-9210 (c) 2018 IEEE. This paper has been accepted by IEEE Transactions on Multimedia. Print ISSN: 1520-9210. Online ISSN: 1941-0077. Preprint link is https://ieeexplore.ieee.org/document/8438958/

In recent years, some attention has been driven to modeling, simulation, and optimization techniques capable of representing and improving discrete event systems. These techniques can support decision making helping to determine the best scenario on a combinatorial search space with stochastic variables. This paper presents findings from a systematic literature review of discrete simulation-based optimization applied to industrial engineering problems. It indicates the most frequent contexts, problems, methods, tools, and intended results of discrete-simulation based studies published in the last 25 years (1991–2016) in scientific journals and conference proceedings. The four research questions presented a scenario to help practitioners and researchers to develop simulation optimization projects for industrial engineering problems. A conclusion presented the gap and prospects found during the writing of the research.

As the rapid development of natural-gas fired units (NGUs), power systems begin to rely more on a natural gas system to supply the primary fuel. On the other hand, natural gas system contingency might cause the nonavailability of NGUs and inevitably jeopardize power system security. To address this issue, this paper studies security-constrained joint expansion planning problems for this combined energy system. We develop a computationally efficient mixed-integer linear programming (MILP) approach that simultaneously considers N-1 contingency in both natural gas system and electricity power system. To reduce the combinatorial search space of MILP models, an extension of a reduced disjunctive model is proposed to decrease the numbers of binary and continuous variables as well as constraints. The involving nonlinear terms in N-1 constraints are exactly linearized without sacrificing any optimality. Numerical results on two typical integrated energy systems demonstrate the necessity of extending N-1 criterion to the whole network of a combined energy system. Experimental results also show that compared with the conventional approach, our proposed MILP approach achieves a great computational performance improvement in solving security-constrained co-optimization expansion planning problems.

Semi-bent Boolean functions are interesting from a cryptographic standpoint, since they possess several desirable properties such as having a low and flat Walsh spectrum, which is useful to resist linear cryptanalysis. In this paper, we consider the search of semi-bent functions through a construction based on cellular automata (CA). In particular, the construction defines a Boolean function by computing the XOR of all output cells in the CA. Since the resulting Boolean functions have the same algebraic degree of the CA local rule, we devise a combinatorial algorithm to enumerate all quadratic Boolean functions. We then apply this algorithm to exhaustively explore the space of quadratic rules of up to 6 variables, selecting only those for which our CA-based construction always yields semi-bent functions of up to 20 variables. Finally, we filter the obtained rules with respect to their balancedness, and remark that the semi-bent functions generated through our construction by the remaining rules have a constant number of linear structures., Comment: 12 pages, 1 figure, submitted to ACRI 2020

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction between model batch size, the choice of hardware accelerator, and variation in the query arrival process.In this paper we introduce InferLine, a system which provisions and manages the individual stages of prediction pipelines to meet end-to-end tail latency constraints while minimizing cost. InferLine consists of a low-frequency combinatorial planner and a high-frequency auto-scaling tuner. The low-frequency planner leverages stage-wise profiling, discrete event simulation, and constrained combinatorial search to automatically select hardware type, replication, and batching parameters for each stage in the pipeline. The high-frequency tuner uses network calculus to auto-scale each stage to meet tail latency goals in response to changes in the query arrival process. We demonstrate that InferLine outperforms existing approaches by up to 7.6x in cost while achieving up to 34.5x lower latency SLO miss rate on realistic workloads and generalizes across state-of-the-art model serving frameworks.

In this paper, we address the exact solution of the vertex bisection problem (VBP). We propose two novel B&B algorithms to solve VBP, which include new upper and lower bound constructive heuristics, and an efficient strategy to explore the combinatorial search space. The computational results show that the proposed algorithms clearly outperforms the state-of-the-art B&B algorithm in quality and efficiency. The two proposed B&B versions differ in the exploration strategy and the storage of the search tree. Also, we provide four new solutions, previously unknown. We consider that the main contributions of this work can be adapted to solve combinatorial problems in other related domains.

Sparse optimization is a central problem in machine learning and computer vision. However, this problem is inherently NP-hard and thus difficult to solve in general. Combinatorial search methods find the global optimal solution but are confined to small-sized problems, while coordinate descent methods are efficient but often suffer from poor local minima. This paper considers a new block decomposition algorithm that combines the effectiveness of combinatorial search methods and the efficiency of coordinate descent methods. Specifically, we consider a random strategy or/and a greedy strategy to select a subset of coordinates as the working set, and then perform a global combinatorial search over the working set based on the original objective function. We show that our method finds stronger stationary points than Amir Beck et al.'s coordinate-wise optimization method. In addition, we establish the convergence rate of our algorithm. Our experiments on solving sparse regularized and sparsity constrained least squares optimization problems demonstrate that our method achieves state-of-the-art performance in terms of accuracy. For example, our method generally outperforms the well-known greedy pursuit method., Comment: to appear in SIGKDD 2020

Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations., 29 pages, 9 figures, 3 tables

BackgroundIntegrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility.ResultsThe presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3,383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems.ConclusionThis new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipe and contains as testing data all the data used in this paper.

In 1983, a computer search was performed for ovals in a projective plane of order ten. The search was exhaustive and negative, implying that such ovals do not exist. However, no nonexistence certificates were produced by this search, and to the best of our knowledge the search has never been independently verified. In this paper, we rerun the search for ovals in a projective plane of order ten and produce a collection of nonexistence certificates that, when taken together, imply that such ovals do not exist. Our search program uses the cube-and-conquer paradigm from the field of satisfiability (SAT) checking, coupled with a programmatic SAT solver and the nauty symbolic computation library for removing symmetries from the search.

In this paper, we show that a large class of diverse problems have a bicomposite structure which makes it possible to solve them with a new type of algorithm called dissection, which has much better time/memory tradeoffs than previously known algorithms. A typical example is the problem of finding the key of multiple encryption schemes with r independent n-bit keys. All the previous error-free attacks required time T and memory M satisfying $$\textit{TM} = 2^{rn}$$ , and even if “false negatives” are allowed, no attack could achieve $$\textit{TM}

Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing Answer Set Programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results., Paper presented at the 34nd International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018, 23 pages, LaTeX, 6 PDF figures

Studying the search behavior of evolutionary many objective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing difficulties with the number of objectives, but also fail to provide the visual information of the evolutionary search. In this paper, we propose a class of scalable test problems, called multi-line distance minimization problem (ML-DMP), which are used to visually examine the behavior of many-objective search. Two key characteristics of the ML-DMP problem are: 1) its Pareto optimal solutions lie in a regular polygon in the two-dimensional decision space, and 2) these solutions are similar (in the sense of Euclidean geometry) to their images in the high-dimensional objective space. This allows a straightforward understanding of the distribution of the objective vector set (e.g., its uniformity and coverage over the Pareto front) via observing the solution set in the two-dimensional decision space. Fifteen well-established algorithms have been investigated on three types of 10 ML-DMP problem instances. Weakness has been revealed across classic multi-objective algorithms (such as Pareto-based, decomposition based and indicator-based algorithms) and even state-of-the-art algorithms designed especially for many-objective optimization. This, together with some interesting observations from the experimental studies, suggests that the proposed ML-DMP may also be used as a benchmark function to challenge the search ability of optimization algorithms. 10.13039/501100000266-Engineering and Physical Sciences Research Council; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000288-Royal Society

Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint’s boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10 D and 30 D taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms.

A subset W of the vertices of a graph G is a resolving set for G when for each pair of distinct vertices u,v in V (G) there exists w in W such that d(u,w)≠d(v,w). The cardinality of a minimum resolving set for G is the metric dimension of G. This concept has applications in many diverse areas including network discovery, robot navigation, image processing, combinatorial search and optimization. The problem of finding metric dimension is NP-complete for general graphs but the metric dimension of trees can be obtained using a polynomial time algorithm. In this paper, we investigate the metric dimension of Cayley graphs on dihedral groups and we characterize a family of them.

This paper presents a quality and distance guided metaheuristic algorithm (QD-ILS) for solving the vertex separation problem. QD-ILS integrates a basic local search procedure with QD-LS strategy, which uses an augmented evaluation function that considers both solution quality and distance between the current solution and the best found solution to guide the search to explore promising regions of the search space. Assessed on two sets of 162 common benchmark instances, QD-ILS achieves highly competitive results in terms of both solution quality and computational efficiency compared with the state-of-the-art algorithms in the literature. Specifically, it improves the previous best known results for 33 out of 162 benchmark instances and matches the best known results on all except four of the remaining instances compared with the state-of-the-art algorithms in the literature. The impact of the distance and quality-based diversification strategy is also investigated.

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

Since its introduction, answer set programming (ASP) has been widely applied to various knowledgeintensive tasks and combinatorial search problems. ASP was found to be closely related to SAT, which has led to new methods of computing answer sets using SAT solvers and techniques adapted from SAT. While this has been the most studied relationship, the relationship of ASP to other computing paradigms, such as constraint satisfaction, quantified boolean formulas (QBF), or first-order logic (FOL) is also the subject of active research. The goal of the annual ASPOCP (acronym for ASP and Other Computing Paradigms) workshop is to facilitate the discussion about crossing the boundaries of current ASP techniques, in combination with or inspired by other computing paradigms. In 2010, the third edition of the ASPOCP workshop (ASPOCP’10) was held in Edinburgh, Scotland, as part of the International Conference on Logic Programming (ICLP) in the frame of the 5th Federated Logic Conference (FLoC). The program included one invited talk by Torsten Schaub and nine regular paper presentations. This special issue of AI Communications contains extended and carefully reviewed versions of select contributions to ASPOCP’10. The articles reflect the range of areas the ASPOCP workshop tries to touch; in particular articles contained in this volume address preferential reasoning, domain-specific heuristics, symmetry breaking, as well as grounding issues. The first article “Potassco: The Potsdam answer set solving collection” [5] is the contribution by our invited speaker and his team. It gives a thorough overview of the suite of ASP tools that are developed at the University of Potsdam. This family of ASP tools

In this paper, we provide an overview of the SAT+CAS method that combines satisfiability checkers (SAT solvers) and computer algebra systems (CAS) to resolve combinatorial conjectures, and present new results vis-a-vis best matrices. The SAT+CAS method is a variant of the Davis–Putnam–Logemann–Loveland DPLL(T) architecture, where the T solver is replaced by a CAS. We describe how the SAT+CAS method has been previously used to resolve many open problems from graph theory, combinatorial design theory, and number theory, showing that the method has broad applications across a variety of fields. Additionally, we apply the method to construct the largest best matrices yet known and present new skew Hadamard matrices constructed from best matrices. We show the best matrix conjecture (that best matrices exist in all orders of the form r2 + r + 1) which was previously known to hold for r ≤ 6 also holds for r = 7. We also confirmed the results of the exhaustive searches that have been previously completed for r ≤ 6.

International audience; Nuclear Magnetic Resonance (NMR) experiments provide distances between nearby atoms of a protein molecule. The corresponding structure determination problem is to determine the 3D protein structure by exploiting such distances. We present a new order on the atoms of the protein, based on information from the chemistry of proteins and NMR experiments, which allows us to formulate the problem as a combinatorial search. Additionally, this order tells us what kind of NMR distance information is crucial to understand the cardinality of the solution set of the problem and its computational complexity.

In this paper, we provide an overview of the SAT+CAS method that combines satisfiability checkers (SAT solvers) and computer algebra systems (CAS) to resolve combinatorial conjectures, and present new results vis-\`a-vis best matrices. The SAT+CAS method is a variant of the Davis$\unicode{8211}$Putnam$\unicode{8211}$Logemann$\unicode{8211}$Loveland $\operatorname{DPLL}(T)$ architecture, where the $T$ solver is replaced by a CAS. We describe how the SAT+CAS method has been previously used to resolve many open problems from graph theory, combinatorial design theory, and number theory, showing that the method has broad applications across a variety of fields. Additionally, we apply the method to construct the largest best matrices yet known and present new skew Hadamard matrices constructed from best matrices. We show the best matrix conjecture (that best matrices exist in all orders of the form $r^2+r+1$) which was previously known to hold for $r\leq6$ also holds for $r=7$. We also confirmed the results of the exhaustive searches that have been previously completed for $r\leq6$., Comment: To appear in Annals of Mathematics and Artificial Intelligence

We proposed a strategy for weighted uncertain interesting pattern mining.It computes expected support confidence to mine weighted correlated patterns.It prunes infrequent patterns early by computing prefix proxy values.It constructs a tree to capture prefix cap & proxy values for uncertain databases.Our strategy generates a manageable number of interesting patterns quickly. Due to a growing demand for efficient algorithms for mining frequent itemsets from uncertain databases, several approaches have been proposed in recent years, but all of them use support-based constraints to prune the combinatorial search space. Most real life databases contain data whose correctness is uncertain. The support-based constraint alone is not enough, because the frequent itemsets may have weak affinity. Even a very high minimum support is not effective for finding correlated patterns with increased weight or support affinity. There are a few approaches in precise databases that propose new measures to mine correlated patterns, but they are not applicable in uncertain databases because certain and uncertain databases differ both semantically and computationally. In this paper, we propose a new strategy: Weighted Uncertain Interesting Pattern Mining (WUIPM), in which a tree structure (WUIP-tree) and several new measures (e.g., uConf, wUConf) are suggested to mine correlated patterns from uncertain databases. To our knowledge, ours is the first work specifically to consider weight or importance of an individual item alongside correlation between items of patterns in uncertain databases. Additionally, we propose a new metric, prefix proxy value, pProxy for our WUIP-tree that helps improve the mining performance. A comprehensive performance study shows that our strategy (a) generates fewer but valuable patterns and (b) is faster than existing approaches even when affinity measures are not applied.

Feature selection, selecting the most informative subset of features, is an important research direction in dimension reduction. The combinatorial search in feature selection is essentially a binary optimization problem, known as NP hard, which can be alleviated by learning feature weights. Traditional feature weights algorithms rely on heuristic search path. These approaches neglect the interaction and dependency between different features, and thus provide no guarantee for optimality. In this paper, we propose a novel joint feature weights learning framework, which imposes both nonnegative and $\ell _{2,1}$ -norm constraints on the feature weights matrix. The nonnegative property ensures the physical significance of learned feature weights. Meanwhile, $\ell _{2,1}$ -norm minimization achieves joint selection of the most relevant features by exploiting the whole feature space. More importantly, an efficient iterative algorithm with proved convergence is designed to optimize a convex objective function. Using this framework as a platform, we propose new supervised and unsupervised joint feature selection methods. Particularly, in the proposed unsupervised method, nonnegative graph embedding is developed to exploit intrinsic structure in the weighted space. Comparative experiments on seven real-world data sets indicate that our framework is both effective and efficient.

Answer set programming is a declarative programming paradigm oriented towards difficult combinatorial search problems. A fundamental task in answer set programming is to compute stable models, i.e., solutions of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has a stable model is $\Sigma^P_2$-complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. Transition systems give a unifying perspective and bring clarity in the description and comparison of solvers. They can be effectively used for analyzing, comparing and proving correctness of search algorithms as well as inspiring new ideas in the design of disjunctive answer set solvers. In this light, we introduce a general template, which accounts for major techniques implemented in disjunctive solvers. We then illustrate how this general template captures solvers DLV, GnT and Cmodels. We also show how this framework provides a convenient tool for designing new solving algorithms by means of combinations of techniques employed in different solvers., Comment: To appear in Theory and Practice of Logic Programming (TPLP)

Since decades, especially in CAD and CG, to solve various kinds of problems and/or to develop automatic systems, not only geometric modeling techniques but also combinatorial searches of geometric elements such as line segments have been applied extensively. Generally it is troublesome and time consuming to program the combinatorial searches for programmers because they are basically algorithmic and it would be difficult to formalize them. In this paper, a new programming technique called IFOG (Inductive Functional prOgramming for Geometric processing) is proposed. IFOG enables to realize easier programing for programmers, especially for beginners, than conventional programming techniques for geometric processing. In IFOG, geometric elements are expressed as their properties, and they are also instances of geometric classes that can be generalized from the instances inductively. Since the classes and instances are stored as text files in a PC, programmers can read and write them whenever they deve...

Automated modeling aims at the induction of mathematical models, both their structure and parameter values, from time-series measurements of observed system variables. In this paper, we address the task of model structure selection, i.e., selecting an optimal structure from a user-specified finite set of alternative model structures, using various approaches to combinatorial search. We propose a mapping of the set of candidate model structures to a fixed-length, vector representation allowing the use of an arbitrary search algorithm as a solver of the structure selection task. We perform a comparative analysis of the performance of thirteen variants of several search algorithms, ranging from ones with high intensification, i.e., focus on neighborhood of the best candidate solutions, to ones with high diversification, i.e., focus on covering the entire search space. The empirical analysis involves eight tasks of reconstructing known models of dynamical systems from synthetic and measured data. The results of the analysis show that search algorithms involving moderate diversification methods have superior performance on the structure selection task. The empirical analysis also reveals that this finding is related to specific properties of the search space of candidate model structures.

The design of cyber-physical systems (CPSs) requires methods and tools that can efficiently reason about the interaction between discrete models, e.g., representing the behaviors of 'cyber' components, and continuous models of physical processes. Boolean methods such as satisfiability (SAT) solving are successful in tackling large combinatorial search problems for the design and verification of hardware and software components. On the other hand, problems in control, communications, signal processing, and machine learning often rely on convex programming as a powerful solution engine. However, despite their strengths, neither approach would work in isolation for CPSs. In this paper, we present a new satisfiability modulo convex programming (SMC) framework that integrates SAT solving and convex optimization to efficiently reason about Boolean and convex constraints at the same time. We exploit the properties of a class of logic formulas over Boolean and nonlinear real predicates, termed monotone satisfiability modulo convex formulas, whose satisfiability can be checked via a finite number of convex programs. Following the lazy satisfiability modulo theory (SMT) paradigm, we develop a new decision procedure for monotone SMC formulas, which coordinates SAT solving and convex programming to provide a satisfying assignment or determine that the formula is unsatisfiable. A key step in our coordination scheme is the efficient generation of succinct infeasibility proofs for inconsistent constraints that can support conflict-driven learning and accelerate the search. We demonstrate our approach on different CPS design problems, including spacecraft docking mission control, robotic motion planning, and secure state estimation. We show that SMC can handle more complex problem instances than state-of-the-art alternative techniques based on SMT solving and mixed integer convex programming.

Fuzzy Answer Set Programming (FASP) is an extension of the popular Answer Set Programming (ASP) paradigm that allows for modeling and solving combinatorial search problems in continuous domains. The recent development of practical solvers for FASP has enabled its applicability to real-world problems. In this paper, we investigate the application of FASP in modeling the dynamics of Gene Regulatory Networks (GRNs). A commonly used simplifying assumption to model the dynamics of GRNs is to assume only Boolean levels of activation of each node. Our work extends this Boolean network formalism by allowing multi-valued activation levels. We show how FASP can be used to model the dynamics of such networks. We experimentally assess the efficiency of our method using real biological networks found in the literature, as well as on randomly-generated synthetic networks. The experiments demonstrate the applicability and usefulness of our proposed method to find network attractors.