Effectiveness of Multi-step Crossover Fusions in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Hanada:2012:CEC,

• title = "Effectiveness of Multi-step Crossover Fusions in Genetic Programming",
• author = "Yoshiko Hanada and Nagahiro Hosokawa and Keiko Ono and Mitsuji Muneyasu",
• pages = "2389--2396",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6256564",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Representation and operators, Discrete and combinatorial optimization.",
• abstract = "Multi-step Crossover Fusion (MSXF) and deterministic MSXF (dMSXF) are promising crossover operators that perform multi-step neighbourhood search between parents, and applicable to various problems by introducing a problem-specific neighbourhood structure and a distance measure. Under their appropriate definitions, MSXF and dMSXF can successively generate offspring that acquire parents' good characteristics along the path connecting the parents. In this paper, we introduce MSXF and dMSXF to genetic programming (GP), and apply them to symbolic regression problem. To optimise trees, we define a neighbourhood structure and its corresponding distance measure based on the largest common subtree between parents with considering ordered/unordered tree structures. Experiments using symbolic regression problem instances showed the effectiveness of a GP with the proposed MSXF and dMSXF.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Yoshiko Hanada Nagahiro Hosokawa Keiko Ono Mitsuji Muneyasu

Citations