A comparison between geometric semantic GP and cartesian GP for boolean functions learning

A. Mambrini, L. Manzoni
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引用次数: 5

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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布尔函数学习中几何语义GP与笛卡尔GP的比较
几何语义遗传规划(GSGP)是遗传规划的一种新形式,与基于标准树的遗传规划相比,它在单输出布尔问题上显示出良好的结果。本文在包含单输出和多输出布尔问题的综合布尔基准集上将GSGP与笛卡尔GP (CGP)进行了比较。结果表明,GSGP也优于CGP,证实了GSGP在解决布尔问题上的有效性。
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