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An Overview of Evolutionary Computation
, 1993
"... Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are current ..."
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Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.
Adapting Crossover in Evolutionary Algorithms
- Proceedings of the Fourth Annual Conference on Evolutionary Programming
, 1995
"... One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mut ..."
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Cited by 79 (0 self)
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One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be self-adaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can a...
An Advanced Evolution Should Not Repeat its Past Errors
, 1996
"... A safe control of genetic evolution consists in preventing past errors of evolution from being repeated. This could be done by keeping track of the history of evolution, but maintaining and exploiting the complete history is intractable. This paper investigates the use of machine learning (ML), in o ..."
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Cited by 20 (6 self)
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A safe control of genetic evolution consists in preventing past errors of evolution from being repeated. This could be done by keeping track of the history of evolution, but maintaining and exploiting the complete history is intractable. This paper investigates the use of machine learning (ML), in order to extract manageable information from this history. More precisely, induction from examples of past trials and errors provides rules discriminating errors from successful trials. Such rules allow to a priori estimate the desirability of future trials; this knowledge can support powerful control strategies. Several strategies of ML-based control are applied to a genetic algorithm, and tested on the Royal Road, a GA-deceptive, and a combinatorial optimization problem. Comparing mutation control with crossover control yields unexpected results. 1 INTRODUCTION Control of evolution aims at keeping some balance between the exploitation and exploration tasks devoted to evolutionary search (G...
Adaptive Genetic Algorithms Based on Fuzzy Techniques
- In Proc. of IPMU'96
, 1996
"... The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algor ..."
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Cited by 14 (0 self)
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The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algorithms are built using fuzzy logic techniques. In this paper, we summarize two types of such approaches. The first one concerns dynamic crossover operators based on parameterized fuzzy connectives and the second one deals with adaptive real-coded genetic algorithms based on the use of fuzzy logic controllers. 1 INTRODUCTION GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities that might eventually evolve into something even better. The loss of critical alleles due to selection pressure, the selection noise, the schemata disruption due to crossover operator, and poor parameter setting may make this exploitation/exploration r...
Transposition versus Crossover: An Empirical Study
, 1999
"... Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms ..."
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Cited by 8 (4 self)
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Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions. 1 INTRODUCTION Genetic Algorithms (GA) are a search paradigm that applies ideas from evolutionary biology ...
A Society of Hill-Climbers
, 1997
"... This paper is concerned with function optimization in binary search spaces. It focuses on how hill-climbers can work together and/or use their past trials in order to speed up the search. A hill-climber is viewed as a set of mutations. The challenge is twofold: one must determine how many bits shoul ..."
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Cited by 7 (0 self)
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This paper is concerned with function optimization in binary search spaces. It focuses on how hill-climbers can work together and/or use their past trials in order to speed up the search. A hill-climber is viewed as a set of mutations. The challenge is twofold: one must determine how many bits should be mutated, and which bits should preferably be mutated, or in other words, which climbing directions are to be preferred. The latter question is addressed by recording the last worst trials of the hill-climbers within an individual termed repoussoir. The hill-climbers further explore the neighborhood of their current point as to get away from the repoussoir. As to the former question, no definite answer is proposed. Nevertheless, we experimentally show that hill-climbers behave quite differently depending on whether one sets a mutation rate pm per bit, or sets the exact number M of bits to mutate per individual. Two algorithms describing societies of hill-climbers, with or without memor...
Controlling Evolution by means of Machine Learning
, 1996
"... A safe control of evolution consists in preventing past errors of evolution to be repeated, which could be done by keeping track of the history of evolution. But maintaining and exploiting the complete history is intractable. This paper therefore investigates the use of machine learning (ML), in ord ..."
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Cited by 4 (1 self)
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A safe control of evolution consists in preventing past errors of evolution to be repeated, which could be done by keeping track of the history of evolution. But maintaining and exploiting the complete history is intractable. This paper therefore investigates the use of machine learning (ML), in order to extract a manageable information from this history. More precisely, induction from examples of past trials and errors provides rules discriminating errors from trials. Such rules allow to a priori estimate the opportunity of next trials; this knowledge can support powerful strategies of control. Several strategies of ML-based control are experimented on the Royal Road, a GAdeceptive and a combinatorial optimization problem. The control of mutations unexpectedly compares to that of crossovers. 1 Introduction Control of evolution aims at keeping some balance between the exploitation and the exploration tasks devoted to evolutionary search [7]. This control involves both the selective pr...
Controlling Genetic Algorithms
, 1996
"... . This paper briefly recalls the principle of genetic algorithms (GA) and discusses their efficiency. The Radcliffe's principles, linking the representation of the problem and the evolution operators, are presented. Last, we address the control of an AG by an induction-based approach. Experimen ..."
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Cited by 2 (1 self)
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. This paper briefly recalls the principle of genetic algorithms (GA) and discusses their efficiency. The Radcliffe's principles, linking the representation of the problem and the evolution operators, are presented. Last, we address the control of an AG by an induction-based approach. Experimental results obtained on typical GA problems and a combinatoric optimization problem, are discussed. Mots cl' es : Algorithme G'en'etique, Evolution, Controle, Optimisation, Apprentissage Inductif, Optimisation Hybride Keywords : Genetic Algorithm, Evolution, Control, Optimization, Inductive Learning, Hybrid Optimization 1 Introduction Les algorithmes de Calcul ' Evolutif (Evolutionary Computation, EC) sont des m'ethodes d'optimisation stochastiques (i.e. fond'ees sur des tirages al'eatoires) , op'erant dans des espaces de taille extremement vaste, et grossi`erement inspir'es de la m'etaphore darwinienne les plus adapt'es survivent. Leur vogue r'ecente ([56, 5, 19, 40, 43, 41]) est due en par...
Revisiting the Memory of Evolution
, 1998
"... . A new evolution scheme is presented, memorizing the extreme (best and worst) past individuals through distributions over the binary search space. These distributions are used to bias the mutation operator in a (¯ + ) Evolution Strategy, guiding the generation of newborn offspring: different mimeti ..."
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Cited by 2 (1 self)
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. A new evolution scheme is presented, memorizing the extreme (best and worst) past individuals through distributions over the binary search space. These distributions are used to bias the mutation operator in a (¯ + ) Evolution Strategy, guiding the generation of newborn offspring: different mimetic strategies are defined, combining either attraction, indifference or repulsion with respect to the two distributions. These distributions are then updated from the best and the worse individuals in the current population. Experiments on large size binary problems allow one to delineate the niche of each of these mimetic strategies. 1. Introduction The powerful process of natural evolution indeed produced biological chefs d'oeuvre. The field of artificial evolution is concerned with transposing and mastering the strengths of evolution within the machine world [24, 45, 12, 28]. A number of applications, ranging from optimization and design problems to adaptation and evolvable hardware, full...
Transposition versus Crossover: An Empirical Study
, 1999
"... Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms ..."
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Cited by 1 (1 self)
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Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions.
