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This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

This article proposes a Memetic Differential Evolution (MDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. The MDE is an adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution (DE) with the exploitative features of two local searchers. The local searchers are adaptively activated by means of a novel control parameter which measures fitness diversity within the population. Numerical results show that the DE framework is efficient for the class of problems under study and employment of exploitative local searchers is helpful in supporting the DE explorative mechanism in avoiding stagnation and thus detecting solutions having a high performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

One of the simplest protocols for clock synchronization in wireless ad hoc and sensor networks is the converge-to-max protocol, which has the simple logic of adjusting each node's clock to be at least as large as any neighbor's. This paper examines the converge-to-max protocol, showing it to be stabilizing even when node clocks have skew, bounded domains, and dynamic communication links. [ABSTRACT FROM AUTHOR]

The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness. [ABSTRACT FROM AUTHOR]

In certain classes of chemical reaction networks (CRN), there may be two stable states. The challenge is to find a model of the CRN such that the stability properties can be predicted. In this paper we consider the problem of building a P-system designed to simulate the CRN in an attempt to determine if the CRN is stable or bistable. We found that for the networks in [2] none of the bistable CRN would have a bistable P-system by stoichiometry alone. The reaction kinetics must be included in the P-system model; the implementation of which has been considered an open problem. In this paper we conclude that a P-system for a CRN in m reactants and n products has at most 2(m2 + mn) membranes and 6(m2 + mn) rules. This suggests that P-system models of a chemical reaction network, including both stoichiometry and reaction kinetics can be built. [ABSTRACT FROM AUTHOR]

A comprehensive view of speech and voice technologies is now demanding better and more complex tools amenable of extracting as much knowledge about sound and speech as possible. Many knowledge-extraction tasks from speech and voice share well-known procedures at the algorithmic level under the point of view of bio-inspiration. The same resources employed to decode speech phones may be used in the characterization of the speaker (gender, age, speaking group, etc.). Based on these facts the present paper examines a hierarchy of sound processing levels at the auditory and perceptual levels on the brain neural paths which can be translated into a bio-inspired audio-processing architecture. Through this paper its fundamental characteristics are analyzed in relation with current tendencies in cognitive audio processing. Examples extracted from speech processing applications in the domain of acoustic-phonetics are presented. These may find applicability in speaker's characterization, forensics, and biometry, among others. [ABSTRACT FROM AUTHOR]

Most of the work about flowshop scheduling problems assume that the problem data are known exactly at the advance or the common approach to the treatment of the uncertainties in the problem is use of probabilistic models. However, the evaluation and optimization of the probabilistic model is computationally expensive and rational only when the descriptions of the uncertain parameters are available from the historical data. In addition, a certain amount of delay on due dates may be tolerated in most real-world situations although they are handled as crisp dates in most of the previous papers. In this paper we deal with a flowshop scheduling problem with fuzzy processing times and flexible due dates. Schedules are generated by a proposed algorithm in the context of ant colony optimization metaheuristic approach. [ABSTRACT FROM AUTHOR]

Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments. [ABSTRACT FROM AUTHOR]

This paper outlines a current lack of consideration for the environmental context of Evolutionary Algorithms used for the generation of music. We attempt to readdress this balance by outlining the benefits of developing strong coupling strategies between agent and environment. It goes on to discuss the relationship between artistic process and the viewer and suggests a placement of the viewer and agent in a shared environmental context to facilitate understanding of the artistic process and a feeling of participation in the work. The paper then goes on to outline the installation ‘Excuse MeÕ and how it attempts to achieve a level of Sonic Ecology through the use of a shared environmental context. [ABSTRACT FROM AUTHOR]

Accurate harmonic estimation is the foundation to ensure a reliable power quality environment in a power system. This paper presents a new algorithm based on a Group Search Optimiser (GSO) to estimate the harmonic components presented in a voltage or current waveform. The structure of harmonic estimation is represented as linear in amplitude and non-linear in phase. The proposed algorithm takes advantage of this feature and estimates amplitudes and phases of harmonics by a linear Least Squared (LS) algorithm and a non-linear GSO-based method respectively. The improved estimation accuracy is demonstrated in this paper in comparison with that of the conventional Discrete Fourier Transform (DFT) and Genetic Algorithms (GAs). Moreover, the performance is still satisfactory even in simulations with the presence of inter-harmonics and frequency deviation. [ABSTRACT FROM AUTHOR]

Due to increasing complexity and non-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In this paper, the stochastic scenario-based risk-return portfolio optimization problem is analyzed and solved with an evolutionary computation approach. The advantage of applying this approach is the creation of a common framework for an arbitrary set of loss distribution-based risk measures, regardless of their underlying structure. Numerical results for three of the most commonly used risk measures conclude the paper. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm, Bacterial Foraging Algorithm with Varying Population (BFAVP), to solve Optimal Power Flow (OPF) problems. Most of the conventional Evolutionary Algorithms (EAs) are based on fixed population evaluation, which does not achieve the full potential of effective search. In this paper, a varying population algorithm is developed from the study of bacterial foraging behavior. This algorithm, for the first time, explores the underlying mechanisms of bacterial chemotaxis, quorum sensing and proliferation, etc., which have been successfully merged into the varying-population frame. The BFAVP algorithm has been applied to the OPF problem and it has been evaluated by simulation studies, which were undertaken on an IEEE 30-bus test system, in comparison with a Particle Swarm Optimizer (PSO) [1]. [ABSTRACT FROM AUTHOR]

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy. [ABSTRACT FROM AUTHOR]

Availability of computer resources is key factor limiting use of bioinformatics analyses as a result of the growing computational demands. Grid computing provides a way to meet these requirements. But it is complicated to build a grid for users. This paper describes an approach to solve this problem using Grid Service technologies. Building the grid based on accepted standards and platforms makes the development and deployment of the grid much easier. A bioinformatics grid computing environment (BioGrid) which consists of the distributed computing application for bioinformatics is presented in this paper. Based on this environment, we propose the architecture of bioinformatics applications which is delivered using Grid Services constructed with the Globus Toolkit 4. We developed a simple program which is defined as the client-server application with grid services. It provides users an approach of grid services to impose grid resources and customize their own grid applications. [ABSTRACT FROM AUTHOR]

Separated grid systems are becoming the new information islands when more and more grid systems are deployed. Grid interoperation is a direction to solve that problem. This paper introduces the implementation of data interoperation between ChinaGrid and SRB. The data interoperation between them is divided into two parts: data access from SRB to ChinaGrid and from ChinaGrid to SRB. Also this paper considers the issues about performance optimization. We get a satisfied experiment result through the optimization measures. [ABSTRACT FROM AUTHOR]

It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in Web information gathering) since duplications and ambiguities of data values (e.g., terms). Rough set based decision tables could be efficient tools for solving this challenge. This paper first illustrates the relationship between decision tables and association mining. It proves that a decision rule is a kind of closed pattern. It also presents an alternative concept of rough association rules to improve the quality of discovered knowledge in this area. The premise of a rough association rule consists of a set of terms (items) and a weight distribution of terms (items). The distinct advantage of rough association rules is that they contain more specific information than normal association rules. This paper also conducts some experiments to compare the proposed method with association rule mining and decision tables; and the experimental results verify that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

This paper investigates a production planning problem in which the inventory is deteriorating at a constant rate, demand is price dependent and back-order is allowed. It is assumed in most previous works that back-ordering customers may not cancel their orders. However, in reality, many customers may withdraw or cancel their orders before receiving their orders. Tacking the cancellation phenomenon into account, this paper develops a continuous-time model to simultaneously determine the production decision and the selling price. A simple algorithm is used to obtain the optimal solutions. Numerical examples are also used to illustrate the solution searching procedure and the characteristics of the optimal decisions. [ABSTRACT FROM AUTHOR]

In this paper, we handle a negotiation method in which a main negotiation consists of multiple sub-negotiations. In items allocation for commerce, there are some risks on the trade because the market balance is determined by supply and demand. The result of main negotiation is also determined by the order of sub-negotiations and agents' behaviors since agents' budgets have limitations on the actual commercial trading. However, it is difficult to decide the order of negotiations, such as simultaneous decisions and rotations. In this paper, we give the trading model in such cases, that is, agents purchase items by pooled buying. In actual trading as pooled buying, items are sold by the volume discount. Concretely, we discuss joint-stock company and private limited partnership on the web. In the negotiation phase, an agent proposes pooled buying based on the number of items and their prices considering their budgets. The degree of risks is calculated. All agents can see the risks with each item. Agents cooperate to the proposing agent based on the degree of risks. In this paper, we give two scenarios for trading. One is to avoid free riders who get surplus without risks. Another one is to promote agents' participation to increase social surplus. For risk aversion and promoting cooperation, we employ the side-payment policy, that is, cooperative agents' risks are preserved to a minimum. Further, we give some discussions where agents must pay the negotiation costs and charge for storage. [ABSTRACT FROM AUTHOR]

This paper presents a new philosophy to model the behavior of a Student in a Tutorial-like system using Learning Automata (LA). The model of the Student in our system is inferred using a higher-level of LA, referred to Meta-LA, which attempt to characterize the learning model of the Students (or Student Simulators), while the latter use the Tutorial-like system. To our knowledge, this is the first published result which attempts to infer the learning model of an LA when it is treated externally as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of Multi-Automata systems, where the Meta-LA communicate with the Students, also modelled using LA, in a synchronous scheme of interconnecting automata. [ABSTRACT FROM AUTHOR]

A novel neural filter and its application to active control of nonlinear noise are proposed in this paper. This method helps to avoid the premature saturation of backpropagation algorithm; meanwhile, guarantees the system convergence by the proposed self-tuning method. The comparison between the conventional filtered-X least-mean-square (FXLMS) algorithm and proposed method for nonlinear broadband noise in active noise cancellation (ANC) system is also made in this paper. The proposed design method is very easy to be implemented and versatile to the other applications. Several simulation results show that the proposed method can effectively cancel the narrowband and nonlinear broadband noise in a duct. [ABSTRACT FROM AUTHOR]

Performance of a genetic algorithm for function optimization, often appeared in real-world applications, depends on its crossover operator strongly. Existing crossover operators are designed for intensive search in certain promising regions. This paper, first, discusses where the promising search regions are on the basis of some assumptions about the fitness landscapes of objective functions and those about a state of a population, and this discussion reveals that existing crossover operators intensively search some of the promising regions but not all of them. Then, this paper designs a new crossover operator for searching all of the promising regions. For utilizing the advantageous features of this crossover operator, a new selection model considering characteristic preservation is also introduced. Several experiments have shown the proposed method has worked effectively on various test functions. [ABSTRACT FROM AUTHOR]

Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN. [ABSTRACT FROM AUTHOR]

This paper proposes a context-based image retrieval system based on color, area, and perimeter intercepted lengths of segmented objects in an image. It characterizes the shape of an object by its area and the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center, and the object color by its mean and standard deviation (STD). Recently, we reported that the color-shape based method (CSBM) is better than conventional color histogram (CCH) and fuzzy color histogram (FCH) in retrieving computer-generated images. However, its performance is only fair in the retrieval of natural images. For CSBM, object color is treated as uniform by reducing the number of colors in an image to only 27 colors. In this paper, we improve the performance by representing the color features of an object with its mean and STD. During the image retrieval stage, t-value is calculated based on the color features of two images, one in the query and the other in the database. The result shows that the proposed method achieves better performance in retrieving natural images compared to CCH, FCH, and CSBM. In the future, the proposed technique will be applied for the retrieval of digitized museum artifacts. [ABSTRACT FROM AUTHOR]

Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems , in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning. [ABSTRACT FROM AUTHOR]

Traditional boosting method like adaboost, boosts a weak learning algorithm by updating the sample weights (the relative importance of the training samples) iteratively. In this paper, we propose to integrate feature re-weighting into boosting scheme, which not only weights the samples but also weights the feature elements iteratively. To avoid overfitting problem caused by feature re-weighting on a small training data set, we also incorporate relevance feedback into boosting and propose an interactive boosting called i.Boosting. It merges adaboost, feature re-weighting and relevance feedback into one framework and exploits the favorable attributes of these methods. In this paper, i.Boosting is implemented using Adaptive Discriminant Analysis (ADA) as base classifiers. It not only enhances but also combines a set of ADA classifiers into a more powerful one. A feature re-weighting method for ADA is also proposed and integrated in i.Boosting. Extensive experiments on UCI benchmark data sets, three facial image data sets and COREL color image data sets show the superior performance of i.Boosting over AdaBoost and other state-of-the-art projection-based classifiers. [ABSTRACT FROM AUTHOR]

This paper addresses questions regarding the decidability of hybrid automata that may be constructed hierarchically and in a modular way, as is the case in many exemplar systems, be it natural or engineered. Since an important step in such constructions is a product operation, which constructs a new product hybrid automaton by combining two simpler component hybrid automata, an essential property that would be desired is that the reachability property of the product hybrid automaton be decidable, provided that the component hybrid automata belong to a suitably restricted family of automata. Somewhat surprisingly, the product operation does not assure a closure of decidability for the reachability problem. Nonetheless, this paper establishes the decidability of the reachability condition over automata which are obtained by composing two semi-algebraic o-minimal systems. The class of semi-algebraic o-minimal automata is not even closed under composition, i.e., the product of two automata of this class is not necessarily a semi-algebraic o-minimal automaton. However, we can prove our decidability result combining the decidability of both semi-algebraic formulæ over the reals and linear Diophantine equations. All the proofs of the results presented in this paper can be found in [1]. [ABSTRACT FROM AUTHOR]

This paper concerns the optimal mode-switching problem in hybrid dynamical systems, where it is desired to compute the switching times between the modes in order to minimize a given cost functional defined on the state trajectory of the system. The state variable cannot be directly observed, and it has to be estimated by an observer. The paper proposes an on-line algorithm and presents bounds on its convergence rate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for formal verification of hybrid systems. To do so, we present a new refinement proof technique, a weak refinement using step invariants. As a case study of the approach, we conduct formal verification of the safety properties of NASA's Small Aircraft Transportation System (SATS) landing protocol. A new model is presented using the timed I/O automata (TIOA) framework [1], and key safety properties are verified. Using the new refinement technique presented in the paper, we first carry over the safety verification results from the previous discrete model studied in [2] to the new model. We also present properties specific to the new model, such as lower bounds on the spacing of aircraft in specific areas of the airspace. [ABSTRACT FROM AUTHOR]

This paper presents an approach to defining distances between nonlinear and hybrid dynamical systems based on formal power series theory. The main idea is that the input-output behavior of a wide range of dynamical systems can be encoded by rational formal power series. Hence, a natural distance between dynamical systems is the distance between the formal power series encoding their input-output behavior. The paper proposes several computable distances for rational formal power series and discusses the application of such distances to various classes of nonlinear and hybrid systems. In particular, the paper presents a detailed discussion of distances for stochastic jump-linear systems. [ABSTRACT FROM AUTHOR]

This paper discusses a new approach to representing a finite automaton as a combination of a linear state equation with a smaller set of free binary variables (i.e., input variables) and binary inequalities, in order to reduce the computational time for solving the model predictive control problem of a class of hybrid systems. In particular, this paper is devoted to proving that a system representation derived by our proposed method is minimal in the sense that the number of its binary input variables is minimal among system models over all linear equivalence transformations that preserve the binary property of free (input) variables. [ABSTRACT FROM AUTHOR]

This paper adapts the Gibbs sampling method to the problem of hybrid system identification. We define a Generalized Linear Hiddenl Markov Model (GLHMM) that combines switching dynamics from Hidden Markov Models, with a Generalized Linear Model (GLM) to govern the continuous dynamics. This class of models, which includes conventional ARX models as a special case, is particularly well suited to this identification approach. Our use of GLMs is also driven by potential applications of this approach to the field of neural prosthetics, where neural Poisson-GLMs can model neural firing behavior. The paper gives a concrete algorithm for identification, and an example motivated by neuroprosthetic considerations. [ABSTRACT FROM AUTHOR]

In this paper, the problem of synthesizing a hybrid controller for a specification expressed as a temporal logic formula φ is considered. We propose a hierarchical approach which consists of three steps. First, the plant to be controlled is abstracted to a fully actuated system. Using the notion of approximate simulation relation, we design a continuous interface allowing the plant to track the trajectories of its abstraction with a guaranteed precision δ. The second step, which is also the main contribution of this paper, consists in deriving a more robust specification φ′ from the temporal logic formula φ such that given a trajectory satisfying φ′, any other trajectory remaining within distance δ satisfies φ. Third, we design a hybrid controller for the abstraction such that all its trajectories satisfy the robust specification φ′. Then, the trajectories of the plant satisfy the original specification. An application to the control of a second order model of a planar robot in a polygonal environment is considered. [ABSTRACT FROM AUTHOR]

This paper presents a motion description language (MDLp) for specifying and encoding autonomous puppetry plays in a manner that is faithful to the way puppetry choreography is currently formulated. In particular, MDLp is a formal language whose strings, when parsed by a dynamical system (the puppet) produces optimized, hybrid control laws corresponding to strings of motions, locations, and temporal durations for each agent. The paper is concerned with the development of this language as well as with an optimization engine for hybrid optimal control of MDLp strings, and with the generation of motion primitives within the "Imitate, Simplify, Exaggerate" puppetry paradigm. [ABSTRACT FROM AUTHOR]

This paper considers the class of hybrid linear second-order oscillating systems, in which two parameters are free to be assigned in a finite set of values. The control task is to decide, at any time instant, the value of the parameters as a function of the system state vector, in order to minimize a quadratic functional over an infinite horizon. The problem lends itself to cope with a variety of important applications, in diverse engineering fields. In the paper a numerical algorithm to compute the optimal switching rule is presented. Then the algorithm is applied to a simplified model of a vehicle suspension system with the aim of minimizing the chassis acceleration (comfort-oriented control). [ABSTRACT FROM AUTHOR]

The Broadcast Language is a programming formalism devised by Holland in 1975, which aims at improving the efficiency of Genetic Algorithms (GAs) during long-term evolution. The key mechanism of the Broadcast Language is to allow GAs to employ an adaptable problem representation. Fixed problem encoding is commonly used by GAs but may limit their performance in particular cases. This paper describes an implementation of the Broadcast Language and its application to modeling biochemical networks. Holland presented the Broadcast Language in his book "Adaptation in Natural and Artificial Systems" where only a description of the language was provided, without any implementation. Our primary motivation for this work was the fact that there is currently no published implementation of the Broadcast Language available. Secondly, no additional examination of the Broadcast Language and its applications can be found in the literature. Holland proposed that the Broadcast Language would be suitable for the modeling of biochemical models. However, he did not support this belief with any experimental work. In this paper, we propose an implementation of the Broadcast Language which is then applied to the modeling of a signal transduction network. We conclude the paper by proposing that with some refinements it will be possible to use the Broadcast Language to evolve biochemical networks in silico. [ABSTRACT FROM AUTHOR]

Researchers have attempted to explain the power of Genetic Programming (GP) search using various notions of schema. However empirical studies of schemas have been limited due to their vast numbers in typical populations. This paper addresses the problem of analyzing schemas represented by tree-fragments. It describes a new efficient way of representing the huge sets of fragments in a population of GP programs and presents an algorithm to find all fragments using this efficient representation. Using this algorithm, the paper presents an initial analysis of fragments in populations of up to 300 programs, each up to seven nodes deep. The analysis demonstrates a surprisingly large variation in the numbers of fragments through evolution and a non-monotonic rise in the most useful fragments. With his method, empirical investigation of the GP building block hypothesis and schema theory in realistic sized GP systems becomes possible. [ABSTRACT FROM AUTHOR]

Learning classifier systems have previously been shown to have some application in single-step tasks. This paper extends work in the area by applying the classifier system to progressively more complex multi-modal test environments, each with typical search space characteristics, convex/non-convex regions of high performance and complex interplay between variables. In particular, two test environments are used to investigate the effects of different degrees of feature sampling, parameter sensitivity, training set size and rule subsumption. Results show that XCSR is able to deduce the characteristics of such problem spaces to a suitable level of accuracy. This paper provides a foundation for the possible use of XCS as an exploratory tool that can provide information from conceptual design spaces enabling a designer to identify the best direction for further investigation as well as a better representation of their design problem through redefinition and reformulation of the design space. [ABSTRACT FROM AUTHOR]

This paper demonstrates that, with minimal modifications, a classifier system can be made to operate just as an ant colony optimizer does for solving the TSP. The paper contains a formal proof of this result, and suggests that the modifications made could be useful in other ways. In effect, the paper suggests that there may be a new role for classifier systems in optimization, inspired by the way that ant colony optimizers have achieved their successes. The paper also suggests that there may be ways suggested by classifier systems to modify ant colony optimization practice. [ABSTRACT FROM AUTHOR]

After two papers comparing ATNoSFERES with XCSM, a Learning Classifier System with internal states, this paper is devoted to a comparison between ATNoSFERES and ACS (an Anticipatory Learning Classifier System). As previously, we focus on the way perceptual aliazing problems encountered in non-Markov environments are solved with both kinds of systems. We shortly present ATNoSFERES, a framework based on an indirect encoding Genetic Algorithm which builds finite-state automata controllers, and we compare it with ACS through two benchmark experiments. The comparison shows that the difference in performance between both system depends on the environment. This raises a discussion of the adequacy of both adaptive mechanisms to particular subclasses of non-Markov problems. Furthermore, since ACS converges much faster than ATNoSFERES, we discuss the need to introduce learning capabilities in our model. As a conclusion, we advocate for the need of more experimental comparisons between different systems in the Learning Classifier System community. [ABSTRACT FROM AUTHOR]

This paper explores the benefits and limitations of using a inspector/executor approach for Software Distributed Shared Memory (SDSM) systems. The role of the inspector is to obtain a description of the address space accessed during the execution of parallel loops. The information collected by the inspector will enable the runtime to optimize the movement of shared data that will happen during the executor phase. This paper addresses the main issues that have been considered to embed an inspector/executor model in a SDSM system: amount of data collected by the inspector, the accurateness of this data when the loop has data and/or control dependences, and the computational overhead introduced. The paper also includes a description of the SDSM system where the inspector/executor model has been embedded. The proposal is evaluated with four applications from the NAS benchmark suite. The evaluation shows that the accuracy of the inspection and the small overheads introduced by the approach allow its use in a SDSM system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal global register allocation. The register allocation problem is expressed as an integer linear programming problem and solved optimally. The model is more flexible than previous graph-coloring based methods and thus allows for register allocations with significantly fewer moves and spills. The formulation can also model complex architectural features, such as bit-wise access to registers. With bit-wise access to registers, multiple subword temporaries can be stored in a single register and accessed efficiently, resulting in a register allocation problem that cannot be addressed effectively with simple graph coloring. The paper describes techniques that can help reduce the problem size of the ILP formulation, making the algorithm feasible in practice. Preliminary empirical results from an implementation prototype are reported. [ABSTRACT FROM AUTHOR]

This paper presents a profiling-based analysis to determine the traversal orientation of link-based tree data structures. Given the very-high memory-hierarchy latencies in modern computers, once the compiler has identified that a pointer-based data structure represents a tree, it would be useful to determine the predominant orientation of traversal for the tree. Optimizing compilers can implement the static shape analysis proposed by Ghiya and Hendren to determine if a linked data structure is a tree [10]. However no techniques have been reported to enable an optimizing compiler to determine the predominant traversal orientation of a tree. This paper describes an analysis that collects data during an instrumented run to determine if the traversal is predominantly breadth-first or depth-first. The analysis determined, with high accuracy, the predominant orientation of traversal of trees in programs written by us as well as in the Olden benchmark suite. This profile-based analysis is storage efficient — it uses only 7% additional memory in comparison with the non-instrumented version of the code. Determining the predominant orientation of traversal of a tree data structure will enable several client optimizations such as improved software-based prefetching, data-storage remapping and better memory allocators. [ABSTRACT FROM AUTHOR]

This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems. [ABSTRACT FROM AUTHOR]

Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. A larger number of replicas require shorter time to reach a replica of the requested resource, but consume more storage of hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for its application. This paper considers the problem for controlling the density of replicas adaptively in dynamic networks. The goal of the problem is to adjust the number of replicas to a constant fraction of the current network size. This paper proposes algorithm inspired by the single species population model, which is a well-known population ecology model. The simulation results show that the proposed algorithm realize self-adaptation of the replica density in dynamic networks. [ABSTRACT FROM AUTHOR]

This paper describes how a distributed neural architecture for the general control of robots has been applied for the generation of a walking behaviour in the Aibo robotic dog. The architecture described has been already demonstrated useful for the generation of more simple behaviours like standing or standing up. This paper describes specifically how it has been applied to the generation of a walking pattern in a quadruped with twelve degrees of freedom, in both simulator and real robot. The main target of this paper is to show that our distributed architecture can be applied to complex dynamic tasks like walking. Nevertheless, by showing this, we also show how a completely neural and distributed controller can be obtained for a robot as complex as Aibo on a task as complex as walking. This second result is by itself a new and interesting one since, to our extent, there are no other completely neural controllers for quadruped with so many DOF that allow the robot to walk. Bio-inspiration is used in three ways: first we use the concept of central pattern generators in animals to obtain the desired walking robot. Second we apply evolutionary processes to obtain the neural controllers. Third, we seek limitations in how real dogs do walk in order to apply them to our controller and limit the search space. [ABSTRACT FROM AUTHOR]

This paper presents a novel Strongly-Typed Genetic Programming approach for building Regression Trees in order to model expressive music performance. The approach consists of inducing a Regression Tree model from training data (monophonic recordings of Jazz standards) for transforming an inexpressive melody into an expressive one. The work presented in this paper is an extension of [1], where we induced general expressive performance rules explaining part of the training examples. Here, the emphasis is on inducing a generative model (i.e. a model capable of generating expressive performances) which covers all the training examples. We present our evolutionary approach for a one-dimensional regression task: the performed note duration ratio prediction. We then show the encouraging results of experiments with Jazz musical material, and sketch the milestones which will enable the system to generate expressive music performance in a broader sense. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of evolutionary search techniques as a mechanism for interactively exploring the design space of 2D painterly renderings. Although a growing body of painterly rendering literature exists, the large number of low-level configurable parameters that feature in contemporary algorithms can be counter-intuitive for non-expert users to set. In this paper we first describe a multi-resolution painting algorithm capable of transforming photographs into paintings at interactive speeds. We then present a supervised evolutionary search process in which the user scores paintings on their aesthetics to guide the specification of their desired painterly rendering. Using our system, non-expert users are able to produce their desired aesthetic in approximately 20 mouse clicks — around half an order of magnitude faster than manual specification of individual rendering parameters by trial and error. [ABSTRACT FROM AUTHOR]

Traditionally, product design problem is usually solved by means of the conjoint analysis methods. However, the conjoint analysis methods suffer from evaluation fatigue. An interactive evolutionary computation (IEC) framework for product design has been thus proposed in this paper. The prediction module taking care of evaluation fatigue is the main part of this framework. In addition, since the evaluation function of product design is an additive utility function, designing operators which heavily utilizes the prediction results becomes possible. The on-chance operator is thus defined in this paper as well. The experimental results indicated the on-chance operator can speed up IEC and improve the quality of solution at the same time. [ABSTRACT FROM AUTHOR]

This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue. [ABSTRACT FROM AUTHOR]