The allowable transitions under simple genetic algorithms and the number of invariant groups
LE3 .A278 2016
Master of Science
Mathematics and Statistics
Mathematics & Statistics
A genetic algorithm is a type of local search that mimics evolution by taking populations, which encode possible solutions, and combines them based on a fitness function to produce individuals that are more fit individuals. This thesis studies the performance of GA’s and estimates the transition probabilities and the number of the invariant groups for some class of Simple Genetic Algorithms GA’s by using an effective mathematical tool known as Markov Chains as well as Hamming distance. The construction of this study is to investigate the performance of some classes of evolutionary algorithms under equal fitness values and different mutation rate.
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