Representations for genetic and evolutionary algorithms. With a foreword by David E. Goldberg.
type: Item
instance of: scholarly article
title: Representations for genetic and evolutionary algorithms. With a foreword by David E. Goldberg.
zbMATH Open document ID: 1030.68083
published in: Studies in Fuzziness and Soft Computing
publication date: 2003-05-22T00:00:00Z
review text: Imitating Darwin's basic life principles, genetic and evolutionary algorithms are often used to solve difficult optimization problems. Starting with a population of candidates, often represented as binary strings, a genetic algorithm selects some of those candidates with a fitness function, then generates new candidates by applying genetic operators, either crossover, where it swaps parts of the bits between a pair of strings; or mutation, where it randomly changes certain bits of a string. This process will be repeated until a preset criterion is met, when the algorithm converges. The ``best string'' produced at the end of this process is declared as the result. The evolutionary algorithm works in basically the same way, except that it allows more flexible coding mechanisms for the candidate representation, more sophisticated selection algorithms to select candidates for expansion, and more genetic operators, which are essentially variants of the two aforementioned operators, to generate new candidates. A key component of this process is a genotype-phenotype mapping, which uses the genetic information (genotype) to construct various properties (phonotype) of the aforementioned candidates. Much work in the GEA area has focused on the part of identifying powerful operators and the associated test problems showing their effectiveness, with the other important area of problem representation being often ignored. On the other hand, we all know the importance of a correct, and proper, problem representation on its final solution, both in theory and practice. This work, an extention of a Ph.D. dissertation, starts a process of filling this cavity. After a thorough investigation of several key aspects of problem representation, it is concluded that a satisfactory problem representation, measured by a high success rate of finding an optimal solution within a reasonable convergence time, should be at least non-, or uniformly, redundant in terms of the average number of genoty…
Mathematics Subject Classification ID: 68T30
Mathematics Subject Classification ID: 68P01
Mathematics Subject Classification ID: 68P30
Mathematics Subject Classification ID: 68Q10
Mathematics Subject Classification ID: 68-01
zbMATH DE Number: 1914867
zbMATH Keywords: genotype-phenotype mapping
zbMATH Keywords: time-quality framework
author: Franz Rothlauf
reviewed by: Zhizhang Shen
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1914867