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K-Bit-Swap: a new operator for real-coded evolutionary algorithms
by
Ter-Sarkisov, Aram
, Marsland, Stephen
in
Artificial Intelligence
/ Clustering
/ Computational Intelligence
/ Control
/ Crossovers
/ Engineering
/ Evolutionary algorithms
/ Exploitation
/ Genes
/ Genetic algorithms
/ Genotype & phenotype
/ Mathematical Logic and Foundations
/ Mechatronics
/ Methodologies and Application
/ Mutation
/ Neural networks
/ Normal distribution
/ Operators
/ Optimization
/ Parents & parenting
/ Robotics
/ Statistical analysis
/ Strings
2017
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K-Bit-Swap: a new operator for real-coded evolutionary algorithms
by
Ter-Sarkisov, Aram
, Marsland, Stephen
in
Artificial Intelligence
/ Clustering
/ Computational Intelligence
/ Control
/ Crossovers
/ Engineering
/ Evolutionary algorithms
/ Exploitation
/ Genes
/ Genetic algorithms
/ Genotype & phenotype
/ Mathematical Logic and Foundations
/ Mechatronics
/ Methodologies and Application
/ Mutation
/ Neural networks
/ Normal distribution
/ Operators
/ Optimization
/ Parents & parenting
/ Robotics
/ Statistical analysis
/ Strings
2017
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Do you wish to request the book?
K-Bit-Swap: a new operator for real-coded evolutionary algorithms
by
Ter-Sarkisov, Aram
, Marsland, Stephen
in
Artificial Intelligence
/ Clustering
/ Computational Intelligence
/ Control
/ Crossovers
/ Engineering
/ Evolutionary algorithms
/ Exploitation
/ Genes
/ Genetic algorithms
/ Genotype & phenotype
/ Mathematical Logic and Foundations
/ Mechatronics
/ Methodologies and Application
/ Mutation
/ Neural networks
/ Normal distribution
/ Operators
/ Optimization
/ Parents & parenting
/ Robotics
/ Statistical analysis
/ Strings
2017
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K-Bit-Swap: a new operator for real-coded evolutionary algorithms
Journal Article
K-Bit-Swap: a new operator for real-coded evolutionary algorithms
2017
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Overview
There have been a variety of crossover operators proposed for real-coded genetic algorithms (RCGAs). Such operators recombine values from pairs of strings to generate new solutions. In this article, we present a recombination operator for RCGAs that selects the string locations for change separately randomly in the parent and offspring, enabling solution parts to move within a string, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a real-world clustering problem. We present two variants of the operator, either selecting bits uniformly at random in both strings or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased toward exploitation of fitness space, the random selection of the second bit for swapping reduces this bias slightly. Statistical analysis of the experimental results using a nonparametric test shows the advantage of the new recombination operators on our test optimization functions.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
Subject
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