Génération et contrôle autonomes d’opérateurs pour les algorithmes évolutionnaires

Les algorithmes évolutionnaires ont largement démontré leur utilité pour la résolution de problèmes combinatoires. Toutefois, leur utilisation pratique suppose de régler, d\u27une part, un certain nombre de paramètres fonctionnels et, d\u27autre part, de définir judicieusement les opérateurs qui seront utilisés pour la résolution. En effet, comme pour la majorité des méthodes métaheuristiques, les performances d\u27un algorithme évolutionnaire sont intrinséquement liées à sa capacité à correctement gérer l\u27équilibre entre l\u27exploitation et l\u27exploration de l\u27espace de recherche. Récemment, de nouvelles approches ont vu le jour pour rendre ces algorithmes plus autonomes, notamment en automatisant le réglage et/ou le contrôle de paramètres. Nous proposons ici une nouvelle méthode dont l\u27objectif est double: d\u27une part nous souhaitons contrôler dynamiquement le comportement des opérateurs au sein d\u27un algorithme génétique, à travers leurs probabilités d\u27application et, d\u27autre part, nous souhaitons gérer un ensemble important d\u27opérateurs potentiels, dont l\u27utilisateur ne connaît pas a priori les performances, de manière également automatisée. Grâce à un mécanisme d\u27évaluation de l\u27état de la recherche en cours et de récompenses et de pénalités, le système devra identifier les opérateurs efficaces au détriment de ceux qui le sont moins. nous expérimentons cette approche sur le problème SAT afin de démontrer qu\u27un algorithme autonome peut obtenir des performances similaires à celles d\u27un algorithme dédié, disposant d\u27opérateurs spécifiquement sélectionnés. Cette démarche vise finalement à libérer l\u27utilisateur de tâches fastidieuses de réglage et de l\u27expertise nécessaire à la conception d\u27algorithmes, souvent ad-hoc

Autonomous operator management for evolutionary algorithms

The performance of an evolutionary algorithm strongly depends on the design of its operators and on the management of these operators along the search; that is, on the ability of the algorithm to balance exploration and exploitation of the search space. Recent approaches automate the tuning and control of the parameters that govern this balance. We propose a new technique to dynamically control the behavior of operators in an EA and to manage a large set of potential operators. The best operators are rewarded by applying them more often. Tests of this technique on instances of 3-SAT return results that are competitive with an algorithm tailored to the problem

Controlling behavioral and structural parameters in evolutionary algorithms

Evolutionary algorithms have been efficiently used for solv-ing combinatorial problems. However a successful application rely on a good definition of the algorithm structure and a correct search guidance. Similarly to the majority of metaheuristic methods, the performance of an evolutionary algorithm is intrinsically linked to its ability to properly manage the balance between the exploitation and the exploration ofthe search space. Recently, new approaches have emerged to make thesealgorithms more independent, especially by automating the setting ofparameters. We propose a new approach whose objective is twofold: (1) to manage an important set of potential operators, whose performances are a priori unknown, and (2) to dynamically control the behavior of operators in a evolutionary algorithm, thanks to their probabilities of application

A Comparison of Operator Utility Measures for On-Line Operator Selection in Local Search

This paper investigates the adaptive selection of operators in the context of Local Search. The utility of each operator is computed from the solution quality and distance of the candidate solution from the search trajectory. A number of utility measures based on the Pareto dominance relationship and the relative distances between the operators are proposed and evaluated on QAP instances using an implied or static target balance between exploitation and exploration. A refined algorithm with an adaptive target balance is then examined

Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection

The goal of adaptive operator selection is the on-line control of the choice of variation operators within evolutionary algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed adaptive operator selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; dynamic multi-armed bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches

Neurophysiology

Contains reports on four research projects.Bell Telephone Laboratories, Inc.U.S. Air Force (Aeronautical Systems Division) under Contract AF 33(615)-1747National Aeronautics and Space Administration (Grant NsG-496)National Institutes of Health (Grant MH-04737-05)National Science Foundation (Grant GP-2495)The Teagle Foundation, Inc

From parameter control to search control: Parameter Control Abstraction in Evolutionary Algorithms

This paper presents a method to encapsulate parameters of evolutionary algorithms and to create an abstraction that simplifies the control and the understanding of the internal behavior of the algorithm. A fuzzy model is used to learn the effects of parameters over the search process. Then, high-level strategies can be defined to modify parameters automatically in order to achieve a scheduledlevel of balance between exploration and exploitation during the search. We experimented supervised control strategies and autonomous schemes that adjust parameters dynamically. Experiments have been performed on the Quadratic Assignment Problem in order to analyze the strengths and weaknesses of each approach. Possible improvements of the general methodology are also discussed