SEMANTIC SEARCH TECHNIQUES FOR LEARNING SMALLER BOOLEAN EXPRESSION TREES IN GENETIC PROGRAMMING
Nicholas Charles Miller, Philip K. Chan · International Journal of Computational Intelligence and Applications · 2014
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.