Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.
Mambrini, A., Manzoni, L. (2014). A comparison between Geometric Semantic GP and Cartesian GP for Boolean functions learning?. In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp.143-144). Association for Computing Machinery [10.1145/2598394.2598475].
A comparison between Geometric Semantic GP and Cartesian GP for Boolean functions learning?
MANZONI, LUCA
2014
Abstract
Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.
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