DocumentCode :
262062
Title :
High-Probability Mutation in Basic Genetic Algorithms
Author :
Croitoru, Nicolae-Eugen
Author_Institution :
Fac. of Comput. Sci., Al.I. Cuza Univ., Iasi, Romania
fYear :
2014
fDate :
22-25 Sept. 2014
Firstpage :
301
Lastpage :
305
Abstract :
Customarily, Genetic Algorithms use lowprobability mutation operators. In an effort to increase their performance, this paper presents a study of Genetic Algorithms with very high mutation rates (≈ 95%) . A comparison is drawn, relative to the low-probability (≈ 1%) mutation GA, on two large classes of problems: numerical functions (well-known test functions such as Rosenbrock´s, Six-Hump Camel Back) and bit-block functions (e.g. Royal Road, Trap Functions). A large number of experimental runs combined with parameter variation provide statistical significance for the comparison. The high-probability mutation is found to perform well on most tested functions, outperforming low-probability mutation on some of them. These results are then explained in terms of dynamic dual encoding and selection pressure reduction, and placed in the context of the No Free Lunch theorem.
Keywords :
genetic algorithms; probability; statistical analysis; bit-block functions; dynamic dual encoding; genetic algorithms; high-probability mutation; no free lunch theorem; numerical functions; parameter variation; selection pressure reduction; statistical significance; Bioinformatics; Genetic algorithms; Genomics; Optimization; Roads; Sociology; Statistics; Genetic Algorithms; Royal Road; high-probability mutation; numerical optimisation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014 16th International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
978-1-4799-8447-3
Type :
conf
DOI :
10.1109/SYNASC.2014.48
Filename :
7034698
Link To Document :
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