A Self-Selecting Crossover Operator

This paper compares the efficacy of different crossover operators for Grammatical Evolution across a typical numeric regression problem and a typical data classification problem. Grammatical evolution is an extension of genetic programming, in that it is an algorithm for evolving complete programs i...

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Bibliographic Details
Published in2006 IEEE International Conference on Evolutionary Computation pp. 1420 - 1427
Main Authors Harper, R., Blair, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Australia
Computer science
Evolutionary computation
Frequency
Genetic algorithms
Genetic mutations
Genetic programming
Problem-solving
Statistical distributions
Stress
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ISBN9780780394872
0780394879
ISSN1089-778X
DOI10.1109/CEC.2006.1688475

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Summary:This paper compares the efficacy of different crossover operators for Grammatical Evolution across a typical numeric regression problem and a typical data classification problem. Grammatical evolution is an extension of genetic programming, in that it is an algorithm for evolving complete programs in an arbitrary language. Each of the two main crossover operators struggles (for different reasons) to achieve 100% correct solutions. A mechanism is proposed, allowing the evolutionary algorithm to self-select the type of crossover utilised and this is shown to improve the rate of generating 100% successful solutions.
ISBN:9780780394872
0780394879
ISSN:1089-778X
DOI:10.1109/CEC.2006.1688475