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- Title
SEMANTIC SEARCH TECHNIQUES FOR LEARNING SMALLER BOOLEAN EXPRESSION TREES IN GENETIC PROGRAMMING.
- Authors
MILLER, NICHOLAS C.; CHAN, PHILIP K.
- Abstract
One sub-field of Genetic Programming (GP) which has gained recent interest is semantic GP, in which programs are evolved by manipulating program semantics instead of program syntax. This paper introduces a new semantic GP algorithm, called SGP+, which is an extension of an existing algorithm called SGP. New crossover and mutation operators are introduced which address two of the major limitations of SGP: large program trees and reduced accuracy on high-arity problems. Experimental results on "deceptive" Boolean problems show that programs created by the SGP+ are 3.8 times smaller while still maintaining accuracy as good as, or better than, SGP. Additionally, a statistically significant improvement in program accuracy is observed for several high-arity Boolean problems.
- Subjects
SEARCH algorithms; BOOLEAN searching; GENETIC programming; SEMANTIC computing; SYNTAX in programming languages; BOOLEAN expressions
- Publication
International Journal of Computational Intelligence & Applications, 2014, Vol 13, Issue 3, p-1
- ISSN
1469-0268
- Publication type
Article
- DOI
10.1142/S1469026814500187