DocumentCode :
3275554
Title :
Gaussian mutation and self-adaption for numeric genetic algorithms
Author :
Hinterding, Robert
Volume :
1
fYear :
1995
fDate :
Nov. 29 1995-Dec. 1 1995
Firstpage :
384
Abstract :
By considering the function variables rather than the binary-bits as genes, new mutation operators can be devised for GAs used to optimise numeric functions. We implement Gaussian mutation operators for genetic algorithms used to optimise numeric functions and show it is superior to bit-flip mutation for most of the test functions. Gaussian mutation is a fundamental operator of both evolutionary strategies (ES) and evolutionary programming (EP). We also implement self-adaptive Gaussian mutation (also used in evolutionary strategies and evolutionary programming) which allows the GA to vary the mutation strength during the run, this gives further improvement on some of the functions. The performance of our GA using a simple implementation of self-adaptive Gaussian mutation is now comparable to ESs. This shows the importance of mutation and the importance of using appropriate mutation operators
Keywords :
Computational modeling; Decoding; Electronic switching systems; Evolutionary computation; Gaussian distribution; Gaussian noise; Genetic algorithms; Genetic mutations; Genetic programming; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA, Australia
Print_ISBN :
0-7803-2759-4
Type :
conf
DOI :
10.1109/ICEC.1995.489178
Filename :
489178
Link To Document :
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