Bi-objective memetic GP with dispersion-keeping Pareto evaluation for real-world regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@Article{LIANG:2020:IS,

• author = "Jiayu Liang and Yu Xue and Jianming Wang",
• title = "Bi-objective memetic {GP} with dispersion-keeping {Pareto} evaluation for real-world regression",
• journal = "Information Sciences",
• volume = "539",
• pages = "16--35",
• year = "2020",
• ISSN = "0020-0255",
• DOI = "doi:10.1016/j.ins.2020.05.136",
• URL = "http://www.sciencedirect.com/science/article/pii/S0020025520305636",
• keywords = "genetic algorithms, genetic programming, Memetic algorithm, Bi-objective GP, Local search, Real-world regression",
• abstract = "Regression tasks aim to determine accurate and simple relationship expressions between variables, which can be regarded as bi-objective optimization problems. As GP (genetic programming) can use expression trees as representation, it is popularly-used for regression. Introducing multi-objective techniques into GP enables it to solve bi-objective tasks, and the success of memetic algorithms show the importance of local search in improving GP. However, existing memetic GP methods are mainly single-objective, in which the local search operators cannot be applied in multi-objective optimization. Moreover, the popularly-used solution evaluation mechanism (Pareto local search) in existing multi-objective memetic methods cannot assure solution dispersion. To handle these problems, a dispersion-keeping Pareto evaluation (DkPE) mechanism is proposed, based on which new crossover and mutation operators adaptive to bi-objective GP are designed. In addition, two base bi-objective GP methods (NSGP (non-dominated sorting GP) and SPGP (strength Pareto GP)) are developed. Applying the new operators in them respectively forms two bi-objective memetic GP methods (MNSGP (memetic NSGP) and MSPGP (memetic SPGP)). Results show that MNSGP and MSPGP outperform NSGP and SPGP respectively, which reflects that DkPE based crossover/mutation increase the performance of NSGP and SPGP. Moreover, solutions evolved by MNSGP outperform reference GP and non-GP based methods",

}

Genetic Programming entries for Jiayu Liang Yu Xue Jianming Wang

Citations