Search

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers. [ABSTRACT FROM AUTHOR]

Directed Steiner Tree (DST) is a central problem in combinatorial optimization and theoretical computer science: Given a directed graph G = (V , E) with edge costs c ∈ R ≥ 0 E , a root r ∈ V and k terminals K ⊆ V , we need to output the minimum-cost arborescence in G that contains an r → t path for every t ∈ K . Recently, Grandoni, Laekhanukit and Li, and independently Ghuge and Nagarajan, gave quasi-polynomial time O (log 2 k / log log k) -approximation Algorithms for the problem, which are tight under popular complexity assumptions. In this paper, we consider the more general Degree-Bounded Directed Steiner Tree (DB-DST) problem, where we are additionally given a degree bound d v on each vertex v ∈ V , and we require that every vertex v in the output tree has at most d v children. We give a quasi-polynomial time (O (log n log k) , O (log 2 n)) -bicriteria approximation: The Algorithm produces a solution with cost at most O (log n log k) times the cost of the optimum solution that violates the degree constraints by at most a factor of O (log 2 n) . This is the first non-trivial result for the problem. While our cost-guarantee is nearly optimal, the degree violation factor of O (log 2 n) is an O (log n) -factor away from the approximation lower bound of Ω (log n) from the set-cover hardness. The hardness result holds even on the special case of the Degree-Bounded Group Steiner Tree problem on trees (DB-GST-T). With the hope of closing the gap, we study the question of whether the degree violation factor can be made tight for this special case. We answer the question in the affirmative by giving an (O (log n log k) , O (log n)) -bicriteria approximation Algorithm for DB-GST-T. [ABSTRACT FROM AUTHOR]

Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize independence, (conditional) entropy, (conditional) mutual information, and total correlation defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property. This turns out to include a number of practically useful cases such as the facility location and set-cover functions. We study specific instantiations of the submodular information measures on these, as well as the probabilistic coverage, graph-cut, log-determinants, and saturated coverage functions and see that they all have mathematically intuitive and practically useful expressions. Finally, we also study generalized independence between subsets of datapoints (random variables in the entropic case), and connect the independence characterizations to independence in log-submodular distributions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy preserving summarization—and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning. We perform real world as well as synthetic experiments on various data summarization tasks. [ABSTRACT FROM AUTHOR]

We study the Maximum Cardinality Matching (MCM) and the Maximum Weight Matching (MWM) problems, on trees and on some special classes of graphs, in the online preemptive and the incremental graph models. In the Online Preemptive model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded, and all rejections are permanent. In this model, the complexity of the problems is settled for deterministic algorithms (McGregor, in: Proceedings of the 8th international workshop on approximation, randomization and combinatorial optimization problems, and proceedings of the 9th international conference on randomization and computation: algorithms and techniques, APPROX'05/RANDOM'05, Springer, Berlin, pp. 170–181, 2005; Varadaraja, in: Automata, languages and programming: 38th international colloquium, ICALP 2011, Zurich, Switzerland, proceedings, part I, pp. 379–390, 2011. https://doi.org/10.1007/978-3-642-22006-7%5f32). Epstein et al. (in: 30th international symposium on theoretical aspects of computer science, STACS 2013, Kiel, Germany, pp. 389–399, 2013. https://doi.org/10.4230/LIPIcs.STACS.2013.389) gave a 5.356-competitive randomized algorithm for MWM, and also proved a lower bound on the competitive ratio of (1 + ln 2) ≈ 1.693 for MCM. The same lower bound applies for MWM. In the Incremental Graph model, at each step an edge is added to the graph, and the algorithm is supposed to quickly update its current matching. Gupta (in: 34th international conference on foundation of software technology and theoretical computer science, FSTTCS 2014, 15–17 Dec 2014, New Delhi, India, pp. 227–239, 2014. https://doi.org/10.4230/LIPIcs.FSTTCS.2014.227) proved that for any ϵ ≤ 1 / 2 , there exists an algorithm that maintains a (1 + ϵ) -approximate MCM for an incremental bipartite graph in an amortized update time of O log 2 n ϵ 4 . No (2 - ϵ) -approximation algorithm with a worst case update time of O(1) is known in this model, even for special classes of graphs. In this paper we show that some of the results can be improved for trees, and for some special classes of graphs. In the online preemptive model, we present a 64 / 33-competitive randomized algorithm (which uses only two bits of randomness) for MCM on trees. Inspired by the above mentioned algorithm for MCM, we present the main result of the paper, a randomized algorithm for MCM with a worst case update time of O(1), in the incremental graph model, which is 3 / 2-approximate (in expectation) on trees, and 1.8-approximate (in expectation) on general graphs with maximum degree 3. Note that this algorithm works only against an oblivious adversary. We derandomize this algorithm, and give a (3 / 2 + ϵ) -approximate deterministic algorithm for MCM on trees, with an amortized update time of O (1 / ϵ) . We also present a minor result for MWM in the online preemptive model, a 3-competitive randomized algorithm (that uses only O(1) bits of randomness) on growing trees (where the input revealed upto any stage is always a tree, i.e. a new edge never connects two disconnected trees). [ABSTRACT FROM AUTHOR]

Crossover operators play a very important role by creation of genetic algorithms (GAs) which are applied in various areas of computer science, including combinatorial optimization. In this paper, fifteen genetic crossover procedures are designed and implemented using a modern C# programming language. The computational experiments have been conducted with these operators by solving the famous combinatorial optimization problem - the quadratic assignment problem (QAP). The results of the conducted experiments on the characteristic benchmark instances from the QAP instances library QAPLIB illustrate the relative performance of the examined crossover operations. All crossover procedures are publicly available with the intention that the GA researchers will choose a procedure which suits the individual demand at the highest degree. [ABSTRACT FROM AUTHOR]

Best-first search can be regarded as anytime scheme for producing lower bounds on the optimal solution, a characteristic that is mostly overlooked. We explore this topic in the context of AND/OR best-first search, guided by the MBE heuristic, when solving graphical models. In that context, the impact of the secondary heuristic for subproblem ordering may be significant, especially in the anytime context. Indeed, our paper illustrates this, showing that a new concept of bucket errors can advise in providing effective subproblem orderings in AND/OR search for both exact and anytime solutions. [ABSTRACT FROM AUTHOR]

Abstract: There are many folded states consistent with a given mountain–valley pattern of equidistant creases on a long paper strip. We would like to fold a paper strip such that the number of paper layers between each pair of hinged paper segments, called the crease width at the crease point, is minimized. This problem is called the stamp folding problem and there are two variants of this problem: minimization of the maximum crease width, and minimization of the total crease width. This optimization problem was recently introduced and investigated from the viewpoint of the counting problem. However, its computational complexity is not known. In this paper, we first show that the minimization problem of the maximum crease width is strongly NP-complete. Hence we cannot solve the problem in polynomial time unless P=NP. We next propose an algorithm that solves the minimization problem. The algorithm itself is a straightforward one, but its analysis is not trivial. We show that this algorithm runs in time where is the number of creases and is the total crease width. [Copyright &y& Elsevier]

Abstract: The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based on computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002)  showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multimodal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems. [Copyright &y& Elsevier]

Abstract: This paper introduces a novel genetic algorithm whose features have been purposely designed to be suited to hardware implementation. This is distinct from previous hardware designs that have been realized directly from conventional genetic algorithm approaches. To be suitable for hardware implementation, we propose that a genetic algorithm should attempt to both minimize final layout dimensions and reduce execution time while remaining a valid implementation. Consequently, the new genetic algorithm specifically aims to keep the requisite silicon area to a minimum by incorporating a monogenetic strategy that retains only the optimal individual, resulting in a dramatic reduction in the memory requirement and obviating the need for crossover circuitry. The results given in this paper demonstrate that new approach improves on a number of existing hardware genetic algorithm implementations in terms of the quality of the solution produced, the calculation time and the hardware component requirements. [Copyright &y& Elsevier]

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA. [ABSTRACT FROM AUTHOR]

To solve the problem of group task allocation with time constraints in open and dynamic network environments, this paper proposes a decentralized combinatorial auction-based approach for group task allocation. In the proposed approach, both resource providers and consumers are modeled as intelligent agents. The proposed approach is decentralized, so all the agents are limited to communicating with their neighboring agents. The proposed approach also allows agents to enter and leave the network environments freely, and is robust for the dynamism and openness of the network environments. Tasks in the proposed approach have deadlines, and may need the collaboration of a group of self-interested providers. The experimental results demonstrate that the proposed approach outperforms two well-known task allocation approaches in terms of success rate of task allocation, the individual utility of the agents, the speed of task allocation and scalability. [ABSTRACT FROM AUTHOR]

The flexibility of spectrum usage required by emerging paradigms, radio spectrum demand driven by new wireless technologies, new applications and government policies oriented to massive use of Internet require spectrum management authorities to have smarter tools to do their work in an efficient way. This paper presents the design and implementation of a web application to support spectrum management activities using constraints programming. The design includes a mathematical model for allocation constraints, a mathematical model for interference constraints, a relational database of the National Allocation Table considering bands, channels and services associated with geopolitical division of Colombia, and an operator allocation database. These tools allow creating and to parameterize different levels of restrictions establishing different costs and weights. The interference analysis module takes into account the geographical location and technical characteristics of transmitting stations. The tool developed in this work can be used, among others, in allocation optimization studies, assessing policies of spectrum reallocation, allocation analysis to maximize specific criteria, reducing technology costs of implementing specific technologies and help identifying objective variables for spectrum price estimation. To facilitate the understanding and exploitation of the application several didactic examples are included. [ABSTRACT FROM AUTHOR]

Thue proved that the factors occurring infinitely many times in square-free words over { 0 , 1 , 2 } avoiding the factors in { 010 , 212 } are the factors of the fixed point of the morphism 0 ↦ 012 , 1 ↦ 02 , 2 ↦ 1 . He similarly characterized square-free words avoiding { 010 , 020 } and { 121 , 212 } as the factors of two morphic words. In this paper, we exhibit smaller morphisms to define these two square-free morphic words and we give such characterizations for six types of binary words containing few distinct squares. [ABSTRACT FROM AUTHOR]

Fat trees are considered suitable structures for data center interconnection networking. Such structures are rigid, and hard to scale up and scale out. A good data center network structure should have high scalability, efficient switch utilization, and high reliability. In this paper we present a class of data center network structures based on hypergraph theory and combinatorial block design theory. We show that our data center network structures are more flexible and scalable than fat trees. Using switches of the same size, our data center network structures can connect more nodes than fat trees, and it is possible to construct different structures with tradeoffs among inter-cluster communication capacity, reliability, the number of switches used, and the number of connected nodes. [ABSTRACT FROM AUTHOR]

Earliness/tardiness scheduling is a new scheduling mode based on the concept of JIT (Just-In-Time).When compared with the traditional job-shop scheduling, earliness/tardiness scheduling involves more complex constraints and thus is a more complicated combinatorial optimization problem. This paper proposes a strategy based on reverse constraints satisfying to improve the genetic algorithm (GA) so that the earliness/tardiness scheduling problem could be solved. The main contents include three parts: (1) the mathematical modeling of earliness/tardiness scheduling is constructed, which is more complex than the common job shop model; (2) the reverse scheduling tactics based on GA are proposed, which includes the GA coding design based on work-piece unit, reversing decoding methods, and the GA fitness evaluation method based on reverse constraints satisfaction; (3) the comparative experiments have been done to show the advantages of reverse scheduling tactic compared with the forward scheduling. The comparative experiment results show the reverse scheduling tactic has practicability and feasibility to solve the job-shop earliness/tardiness scheduling problem. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification; a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness. [ABSTRACT FROM AUTHOR]

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark. [ABSTRACT FROM AUTHOR]

A new state space representation for a class of combinatorial optimization problems, related to minimal Hamiltonian cycles, enables efficient implementation of exhaustive search for the minimal cycle in optimization problems with a relatively small number of vertices and heuristic search for problems with large number of vertices. This paper surveys structures for representing Hamiltonian cycles, the use of these structures in heuristic optimization techniques, and efficient mapping of these structures along with respective operators to a newly proposed electrooptical vector by matrix multiplication (VMM) architecture. Record keeping mechanisms are used to improve solution quality and execution time of these heuristics using the VMM. Finally, the utility of a low-power VMM based implementation is evaluated. [ABSTRACT FROM AUTHOR]

Abstract: We introduce the vertex cover () problem, that is, the problem of finding a minimum weight set such that the graph has no , where is a path with vertices. The problem also has its application background. In this paper, we first show that the problem is NP-hard for any integer . Then we restrict our attention to the problem and give a 2-approximation algorithm using the primal–dual method. [Copyright &y& Elsevier]

Abstract: Vertex coloring problem is a combinatorial optimization problem in which a color is assigned to each vertex of the graph such that no two adjacent vertices have the same color. Cellular learning automata (CLA) is an effective probabilistic learning model combining cellular automata and learning automata. Irregular cellular learning automata (ICLA) is a generalization of cellular learning automata in which the restriction of rectangular grid structure in traditional CLA is removed. In this paper, an ICLA-based algorithm is proposed for finding a near optimal solution of the vertex coloring problem. The proposed coloring algorithm is a fully distributed algorithm in which each vertex chooses its optimal color based solely on the colors selected by its adjacent vertices. The time complexity of the proposed algorithm is computed for finding a optimal solution of the vertex coloring problem in an arbitrary graph. To show the superiority of our proposed algorithm over the existing methods, simulation experiments have been conducted. The obtained results show that the proposed algorithm outperforms the others in terms of the required number of colors and running time of algorithm. [Copyright &y& Elsevier]

Detection of copositivity plays an important role in combinatorial and quadratic optimization. Recently, an algorithm for copositivity detection by simplicial partition has been proposed. In this paper, we develop an improved depth-first simplicial partition algorithm which reduces memory requirements significantly and therefore enables copositivity checks of much larger matrices - of size up to a few thousands instead of a few hundreds. The algorithm has been investigated experimentally on a number of MaxClique problems as well as on generated random problems. We present numerical results showing that the algorithm is much faster than a recently published linear algebraic algorithm for copositivity detection based on traditional ideas - checking properties of principal sub-matrices. We also show that the algorithm works very well for solving MaxClique problems through copositivity checks. [ABSTRACT FROM AUTHOR]

The problem Local Search, which finds a local minimum of a black-box function on a given graph, is of both practical and theoretical importance to combinatorial optimization, complexity theory, and many other areas in theoretical computer science. In this paper, we study the problem in both the randomized and the quantum query models and give new lower and upper bound techniques in both models. The lower bound technique works for any graph that contains a product graph as a subgraph. Applying it to the Boolean hypercube {0, 1}n and the constant-dimensional grids [n]d, two particular product graphs that recently drew much attention, we get the following tight results: RLS({0, 1}n) = ϴ(2n/2n1/2), QLS({0, 1}n) = ϴ(2n/3n1/6), RLS([n]d) = ϴ(nd/2) for d ≥ 4, QLS([n]d) = ϴ(nd/3) for d ≥ 6. Here RLS(G) and QLS(G) are the randomized and quantum query complexities of Local Search on G, respectively. These improve the previous results by Aaronson [in Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, 2004, pp. 465-474], Ambainis (unpublished), and Santha and Szegedy [in Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, 2004, pp. 494-501]. Our new algorithms work well when the underlying graph expands slowly. As an application to [n]2, a new quantum algorithm using O(√n(log log n)1.5) queries is given. This improves the previously best known upper bound of O(n2/3) (see Aaronson [in Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, 2004, pp. 465-474]), and implies that Local Search on grids exhibits different properties in low dimensions. [ABSTRACT FROM AUTHOR]

Abstract: The matter of using scheduling algorithms in parallel computing environments is discussed in this paper. There are proposed methods of parallelizing the criterion function calculations for a single solution and a group of concentrated solutions (local neighborhood) dedicated to being used in metaheuristic approaches. Also a parallel scatter-search metaheuristic is proposed as a multiple-thread approach. Computational experiments are done for the flow shop, the classic NP-hard problem of the combinatorial optimization. [Copyright &y& Elsevier]

Consider the scenario where we are given an ideal functionality of oblivious transfer (OT), and we wish to construct a larger OT by invoking the above functionality as a black box. How many invocations of an ideal OT functionality are necessary? In tackling this problem, some lower bounds were derived using entropy previously. In this paper, we manage to achieve tighter lower bounds by employing a combinatorial approach. This new approach yields lower bounds which are two times larger than the existing bounds. [ABSTRACT FROM AUTHOR]

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble. [ABSTRACT FROM AUTHOR]

Abstract: Trade-off (aka Pareto) curves are typically used to represent the trade-off among different objectives in multiobjective optimization problems. Although trade-off curves are exponentially large for typical combinatorial optimization problems (and infinite for continuous problems), it was observed in Papadimitriou and Yannakakis [On the approximability of trade-offs and optimal access of web sources, in: Proc. 41st IEEE Symp. on Foundations of Computer Science, 2000] that there exist polynomial size approximations for any , and that under certain general conditions, such approximate -Pareto curves can be constructed in polynomial time. In this paper we seek general-purpose algorithms for the efficient approximation of trade-off curves using as few points as possible. In the case of two objectives, we present a general algorithm that efficiently computes an -Pareto curve that uses at most 3 times the number of points of the smallest such curve; we show that no algorithm can be better than 3-competitive in this setting. If we relax to any , then we can efficiently construct an -curve that uses no more points than the smallest -curve. With three objectives we show that no algorithm can be c-competitive for any constant c unless it is allowed to use a larger value. We present an algorithm that is 4-competitive for any . We explore the problem in high dimensions and give hardness proofs showing that (unless P=NP) no constant approximation factor can be achieved efficiently even if we relax by an arbitrary constant. [Copyright &y& Elsevier]

Mathematical-modeling and constraint-programming languages have orthogonal strengths in stating combinatorial optimization problems. Modeling languages typically feature high-level set and algebraic notations, while constraint-programming languages provide a rich constraint language and the ability to specify search procedures. This paper shows that many of the functionalities typically found in modeling languages can be integrated elegantly in constraint-programming libraries without defining a specific language or preprocessor In particular, it presents the design of MODELER, a C++ modeling layer for constraint programming which demonstrates how to enhance the expressiveness of constraint-programming libraries and to bridge much of the gap between libraries and modeling languages. [ABSTRACT FROM AUTHOR]

In this paper, we have investigated the problem of gender classification using frontal facial images. Four different classifiers, namely K-means, k-nearest neighbors, Linear Discriminant Analysis and Mahalanobis Distance Based classifiers are compared. Receiver operating characteristics (ROC) curve along with the area under the convex hull (AUCH) have been utilized as the performance measures of the classifiers at different feature subsets. To measure the overall performance of a classifier with single scalar value, the new scheme of finding the area under the convex hull of AUCH of ROC curves (AUCH of AUCHS) is proposed. It has been observed that, when the number of macro features is increased beyond 5, the AUCH saturates and even decreases for some classifiers, illustrating the curse of dimensionality. We then used genetic programming to combine classifiers and thus evolved an optimum combined classifier (OCC), producing better performance than the individual classifiers. We found that using only two features, the OCC has comparable performance to that of original classifier using 20 macro features. It produces true positive rate values as high as 0.94 corresponding to false positive rate as low as 0.15 for 1: 3 train to testing ratio. We also observed that heterogeneous combination of classifiers is more promising than the homogenous combination. [ABSTRACT FROM AUTHOR]

This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs). In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction. Daily predictions are conducted and prediction accuracy is measured. In this study, three feature transformation methods for ANNs are compared. Comparison of the results achieved by a feature transformation method using the GA to the other two feature transformation methods shows that the performance of the proposed model is better. Experimental results show that the proposed approach reduces the dimensionality of the feature space and decreases irrelevant factors for stock market prediction. [ABSTRACT FROM AUTHOR]

A polygonal approximation captures the essential features of a digital planar curve and yields a compact representation. Those points of the digital curve that carry vital information about the shape of the curve form the vertices of the approximating polygon and are called significant vertices. In this paper, we present a genetic algorithm-based approach to locate a specified number of significant points, such that the approximation error between the original curve and its polygonal version obtained by joining the adjacent significant points is minimized. By using a priori knowledge about the shape of the curve we confine our search to only those points of the curve that have the potential of qualifying as significant points. We also incorporate chromosome differentiation to improve upon the effectiveness of the search in arriving at a near-optimal polygonal approximation. Finally, we show that the proposed method performs remarkably well when evaluated in terms of the metrics available for assessing the goodness of a polygonal approximation algorithm. [ABSTRACT FROM AUTHOR]

When genetic algorithms are applied to large-scale optimization problems taking only integer-valued solutions, too often the chromosomes generated faily to satisfy the constraint conditions. In such cases, the selection operator is unable to produce high-quality chromosomes, worsening the search efficiency. As a consequence, many times good solutions cannot be obtained. In this paper, in order to cope with such a problem, a new algorithm with improved chromosome selection mechanism is proposed, taking into account the evaluation function including some information about chromosomes not contained in the feasible region of the nonlinear integer programming problem under consideration. The proposed method considers how far each unfeasible chromosome is from the feasible region. The improved algorithm was applied to a large-scale problem of optimal design of system realibility formulated as a nonlinear integer programming problem and numerical experiments confirmed the effectiveness of the method. [ABSTRACT FROM AUTHOR]