Exploring and Exploiting Models of the Fitness Landscape: a Case Against Evolutionary Optimization

In recent years, the theories of natural selection and biological evolution have proved popular metaphors for understanding and solving optimization problems in engineering design. This thesis identifies some fundamental problems associated with this use of such metaphors. Key objections are the failure of evolutionary optimization techniques to represent explicitly the goal of the optimization process, and poor use of knowledge developed during the process. It is also suggested that convergent behaviour of an optimization algorithm is an undesirable quality if the algorithm is to be applied to multimodal problems. An alternative approach to optimization is suggested, based on the explicit use of knowledge and/or assumptions about the nature of the optimization problem to construct Bayesian probabilistic models of the surface being optimized and the goal of the optimization. Distinct exploratory and exploitative strategies are identified for carrying out optimization based on such models—exploration based on attempting to reduce maximally an entropy-based measure of the total uncertainty concerning the satisfaction of the optimization goal over the space, exploitation based on evalutation of the point judged most likely to achieve the goal—together with a composite strategy which combines exploration and exploitation in a principled manner. The behaviour of these strategies is empirically investigated on a number of test problems. Results suggest that the approach taken may well provide effective optimization in a way which addresses the criticisms made of the evolutionary metaphor, subject to issues of the computational cost of the approach being satisfactorily addressed

Induction-based control of genetic algorithms

International audienceThis paper presents a Machine Learning approach to control genetic algorithms. From examples gathered through spying evolution or experimenting on populations, induction extracts a rule-based characterization of which evolutionary events are good or bad for evolution. Such rule base allows for further generations to escape most disruptive or unproductive changes, according to a civilized rather than Darwinian evolution scheme. An evolutionary event is described as mutating a chromosome (at given bit—string positions) or crossing over two chromosomes (with given crossing points), and labeled by comparing the fitness of the offspring with that of its parents. Knowledge induced from such events allows to predict the effects of further operators, thereby filtering further undesirable events. Experimentations on some artificial problems are discussed

An Induction-based Control for Genetic Algorithms (Extended Abstract)

Michele Sebag 1;3 and Marc Schoenauer 2;3 and Caroline Ravise 1;3 1 LMS, Ecole Polytechnique, 91128 Palaiseau, France 2 CMAP, Ecole Polytechnique, 91128 Palaiseau, France 3 LRI, Universit'e de Paris-Sud, Batiment 490, F-91405 Orsay, France Abstract. This paper presents an induction-based control of genetic algorithms: 1- examples of the behavior of the genetic operators (crossover and mutation) are gathered; 2- rules characterizing disruptive operators are induced from the gathered examples; 3- last, these rules are used to reject operators classified disruptive. Evolution is thereby speeded up. Experimental results on the well-known Royal Road problem and on a GA-deceptive problem are presented. 1 Introduction Genetic Algorithms (GAs) are widely known as powerful optimization algorithms [1]. As such, they have been applied in the Machine Learning community, mainly to build classifiers systems since the seminal work of Holland [3]. In contrast with using GAs to reach ML goa..