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The Advantages of Evolutionary Computation
, 1997
"... Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific ..."
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Cited by 396 (5 self)
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Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as the ability to selfadapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine. 1 Introduction Darwinian evolution is intrinsically a robust search and optimization mechanism. Evolved biota demonstrate optimized complex behavior at every level: the cell, the organ, the individual, and the population. The problems that biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivities. These are also characteristics of problems that have proved to be especially intractable to classic methods of o...
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 298 (11 self)
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Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores populationbased incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better. Extensions to this algorithm are discussed and analyzed. PBIL and extensions are compared with a standard GA on twelve problems, including standard numerical optimization functions, traditional GA test suite problems, and NPComplete problems.
A survey of constraint handling techniques in evolutionary computation methods
 Proceedings of the 4th Annual Conference on Evolutionary Programming
, 1995
"... One of the major components of any evolutionary system is the eval� uation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary com� putation techniques assume the existence of an �e�cient � evaluation function for feasible individ ..."
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Cited by 74 (3 self)
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One of the major components of any evolutionary system is the eval� uation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary com� putation techniques assume the existence of an �e�cient � evaluation function for feasible individuals � there is no uniform methodology for handling �i.e. � evaluating � unfeasible ones. The simplest approach� incorporated by evolution strategies and a version of evolutionary programming �for numerical optimization problems� � is to reject un� feasible solutions. But several other methods for handling unfeasible individuals have emerged recently. This paper reviews such methods �using a domain of nonlinear programming problems � and discusses their merits and drawbacks. 1
Genocop III: A Coevolutionary Algorithm for Numerical Optimization Problems with Nonlinear Constraints
"... During the last two years several methods have been proposed for handling nonlinear constraints by genetic algorithms for numerical optimization problems; most of them were based on penalty functions. However, the performance of these methods is highly problemdependent; moreover, many methods requi ..."
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Cited by 65 (12 self)
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During the last two years several methods have been proposed for handling nonlinear constraints by genetic algorithms for numerical optimization problems; most of them were based on penalty functions. However, the performance of these methods is highly problemdependent; moreover, many methods require additional tunning of several parameters. In this paper we present a new optimization system (Genocop III), which is based on concepts of coevolution and repair algorithms. We present the resulfs of the system on a few selected test problems and discuss some directions for furlher research.
Revisiting Evolutionary Programming
, 1998
"... Evolutionary programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measured by current standards. This paper provides a series of experiments that follow th ..."
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Cited by 55 (2 self)
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Evolutionary programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measured by current standards. This paper provides a series of experiments that follow the framework of the original approach from the early 1960s, brought up to date with current computing machinery. A brief review of evolutionary programming and its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies, is also offered. Keywords: evolutionary programming, evolutionary computation, forecasting, control. 1. INTRODUCTION There are three main lines of investigation within the current framework of evolutionary computation: (1) genetic algorithms, (2) evolution strategies, and (3) evolutionary programming. Reviews of these methods are offered in several recent books 15 . Each of these methods has developed over more ...
Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitve Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within ..."
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Cited by 30 (0 self)
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Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform b...
An Empirical Investigation of MultiParent Recombination Operators in Evolution Strategies
, 1997
"... An extension of evolution strategies to multiparent recombination involving a variable number % of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differring in their modality, separability and the regular, respectively i ..."
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Cited by 30 (2 self)
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An extension of evolution strategies to multiparent recombination involving a variable number % of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differring in their modality, separability and the regular, respectively irregular arrangement of their local optima. Multiparent diagonal crossover, uniform scanning crossover, and a multiparent version of intermediary recombination are considered in the experiments. The performance of the algorithm is observed to depend on the particular combination of recombination operator and objective function. In most of the cases a significant increase of performance is observed as the number of parents increases. However, there might also be no significant impact of recombination at all, and for one of the unimodal objective functions the performance is observed to deteriorate over the course of evolution for certain choices of the recombination operator and the number ...
Evolutionary Programming Using Mutations Based on the Lévy Probability Distribution
, 2004
"... This paper studies evolutionary programming with mutations based on the Lvy probability distribution. The Lvy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutati ..."
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Cited by 29 (8 self)
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This paper studies evolutionary programming with mutations based on the Lvy probability distribution. The Lvy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter in the Lvy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Lvy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.
On Evolutionary Exploration and Exploitation
, 1998
"... . Exploration and exploitation are the two cornerstones of problem solving by search. The common opinion about evolutionary algorithms is that they explore the search space by the (genetic) search operators, while exploitation is done by selection. This opinion is, however, questionable. In this ..."
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Cited by 26 (0 self)
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. Exploration and exploitation are the two cornerstones of problem solving by search. The common opinion about evolutionary algorithms is that they explore the search space by the (genetic) search operators, while exploitation is done by selection. This opinion is, however, questionable. In this paper we give a survey of different operators, review existing viewpoints on exploration and exploitation, and point out some discrepancies between and problems with current views. 1. Introduction Evolutionary algorithms (EA) belong to the family of stochastic generateandtest search algorithms [28]. There are different types of EAs, the most common classification distinguishes Genetic Algorithms (GA), Evolution Strategies (ES) and Evolutionary Programming (EP), [4]. A fourth type of EA, Genetic Programming (GP) has grown out of GAs and is often seen as a subclass of them. Besides the different historical roots and philosophy there are also technical differences between the three mai...
An Alternative Explanation for the Manner in which Genetic Algorithms Operate
 BioSystems
, 1997
"... The common explanation of the manner in which genetic algorithms (GAs) process individuals in a population of contending solutions relies on the "building block hypothesis." This suggests that successively better solutions are generated by combining useful parts of extant solutions. An alternativ ..."
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Cited by 25 (10 self)
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The common explanation of the manner in which genetic algorithms (GAs) process individuals in a population of contending solutions relies on the "building block hypothesis." This suggests that successively better solutions are generated by combining useful parts of extant solutions. An alternative explanation is presented which focuses on the collective phenomena taking place in populations that undergo recombination. The new explanation is derived from investigations in evolution strategies (ESs). The principles studied are general, and hold for all evolutionary algorithms (EAs), including genetic algorithms (GAs). Further, they appear to be somewhat analogous to some theories and observations on the benefits of sex in biota. Keywords building block hypothesis, evolutionary algorithms, multirecombination 1 Introduction Although specific theoretical investigations into the properties of genetic algorithms have gained considerable recent attention, there still is no satisfa...