Metaheuristics Based on Solution Recombination

Günther Zäpfel (), Roland Braune () and Michael Bögl ()
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Günther Zäpfel: Universität Linz
Roland Braune: Universität Linz
Michael Bögl: Universität Linz

Chapter Chapter 7 in Metaheuristic Search Concepts, 2010, pp 121-143 from Springer

Abstract: Abstract Genetic Algorithms (GA) perform the search by solution recombination and belong to the group of Evolutionary Algorithms (EA). As the name suggests, they imitate the process of evolution and use the idea of natural selection, recombination and mutation to control the search process. Because Genetic Algorithms use ideas coming from the theory of evolution, we have to introduce the terms used in context Genetic Algorithms first. In section 3.3 it was shown how search by recombination can basically be performed and that multiple solutions must be used, else recombination is not possible. The set of solutions is called population in the context of Genetic Algorithm. Consequently, a single solution of this population is called individual or chromosome. Chromosomes are made of genes and the values of a gene are called alleles. A gene is a property of a solution, which can take values of a predefined domain. The position of a gene is called locus. If a gene has taken a certain value it is called allele. So, the genes of a chromosome describe the genotype of an individual. A phenotype describes a certain individual with all its states, i. e. all genes have fixed alleles (cf. [96]).

Keywords: Genetic Algorithm; Candidate Solution; Mutation Operator; Knapsack Problem; Premature Convergence (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-3-642-11343-7_7

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