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An investigation of messy genetic algorithmsGenetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.
Document ID
19910003773
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Goldberg, David E. (Alabama Univ. Tuscaloosa., United States)
Deb, Kalyanmoy (Alabama Univ. Tuscaloosa., United States)
Korb, Bradley (Alabama Univ. Tuscaloosa., United States)
Date Acquired
September 6, 2013
Publication Date
May 1, 1990
Subject Category
Computer Programming And Software
Report/Patent Number
NAS 1.26:187268
NASA-CR-187268
Accession Number
91N13086
Funding Number(s)
CONTRACT_GRANT: NCC9-16
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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