An Effective Many-Objective Extension of Genetic Programming and Its Benchmark

Created by W.Langdon from gp-bibliography.bib Revision:1.8154

@InProceedings{Ohki:2019:DDP,

• author = "Makoto Ohki",
• title = "An Effective Many-Objective Extension of Genetic Programming and Its Benchmark",
• booktitle = "2019 First International Conference on Digital Data Processing (DDP)",
• year = "2019",
• pages = "92--96",
• month = nov,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/DDP.2019.00027",
• abstract = "This paper describes many-objective extension (MaO extension) of GP problem. The extension reduces a bloat phenomena what often happens in GPs. MaO extension of GP consists of addition of objective functions, a partial sampling (PS) operator, a structural distance, elimination of identical trees and subset size scheduling. In this paper, MaO extension of GP are applied to NSGA-II and SPEA2. Because few researchers have considered effective benchmark problems for many-objective genetic programming (MaOGP), MaOGP is less developed than the many-objective evolutionary algorithm (MaOEA). This paper proposes a new benchmark problem for MaOGP. The benchmark problem is based on the many-objective knapsack problem (MaOKSP). In the benchmark, a function given by GP returns a binary vector for MaOKSP. A research on such benchmark problems contributes to the future development of MaOGPs.",
• notes = "Also known as \cite{8948757}",

}

Genetic Programming entries for Makoto Ohki

Citations