An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The role of mutation and recombination in evolutionary algorithms
The role of mutation and recombination in evolutionary algorithms
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Mutation-crossover isomorphisms and the construction of discriminating functions
Evolutionary Computation
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The adaptive landscape has become a standard approach for genetic algorithm visualization, and the representation of the higher dimensional chromosome space onto a two-dimensional plane suitable for the construction of an adaptive landscape requires an accurate measurement of the distance between chromosomes. Although the shortcomings of traditional approaches to adaptive landscape construction are by no means unknown to the research community, the intuitions afforded by this visualization have kept it in widespread usage. Since the multidimensional scaling required for the creation of a representative landscape is often disregarded to avoid the computational overhead required, this paper demonstrated that distance measures are available that remain representative of the genetic operators of the genetic algorithm while being suitable for multidimensional scaling techniques. This paper also demonstrated that in spite of the complications expected when the distance between chromosomes is measured with respect to both a unary mutation operation and a binary recombination operation simultaneously, it is possible to construct adaptive landscapes that depict features indicative of the effects of both genetic operators.