Dissertation/Thesis Abstract

Problems with crossover bias for binary string representations in genetic algorithms
by Cleary, Brian, M.S., California State University, Long Beach, 2011, 57; 1504439
Abstract (Summary)

Genetic algorithms, a popular technique for optimization, traditionally uses binary strings to represent problem parameters and uses crossover and mutation operators based on this binary string representation. However, crossover operations on binary strings result in a highly biased set of possible outcomes, causing some values to have selective pressure separate from the fitness functions representing the optimization problem. This effect is particularly problematic for categorical variables where no notion of genetic similarity applies, thus no fitness selective pressure will be applied to make up for the bias against some values. For this reason, binary strings should not be used in genetic algorithms, especially for categorical variables.

Indexing (document details)
Advisor: Lam, Shui-Fung
School: California State University, Long Beach
School Location: United States -- California
Source: MAI 50/03M, Masters Abstracts International
Subjects: Computer science
Publication Number: 1504439
ISBN: 978-1-124-99371-3
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