Cooperative Coevolution of Automatically Defined Functions with Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Sosa-Ascencio:2012:MICAI,

• author = "Alejandro Sosa-Ascencio and Manuel Valenzuela-Rendon and Hugo Terashima-Marin",
• booktitle = "11th Mexican International Conference on Artificial Intelligence (MICAI 2012)",
• title = "Cooperative Coevolution of Automatically Defined Functions with Gene Expression Programming",
• year = "2012",
• pages = "89--94",
• address = "San Luis Potosi",
• month = oct # " 27-" # nov # " 4",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, regression analysis, ADF, GEP, automatically defined function, cooperative coevolution, evolutionary approach, gene expression programming, symbolic regression problem, vgGA framework, virtual gene genetic algorithm, Biological cells, Indexes, Mathematical model, Sociology, Statistics, automatically defined functions, cooperative coevolution, symbolic regression problems",
• isbn13 = "978-1-4673-4731-0",
• DOI = "doi:10.1109/MICAI.2012.15",
• abstract = "The decomposition of problems into smaller elements is a widespread approach. In this paper we consider two approaches that are based over the principle to segmentation to problems for the resolution of resultant sub-components. On one hand, we have Automatically Defined Functions (ADFs), which originally emerged as a refinement of genetic programming for reuse code and modularise programs into smaller components, and on the other hand, we incorporated co evolution to the implementation of ADFs, we present a cooperative co evolutionary-based approach to the problem of developing ADFs, we implemented a module of Gene Expression Programming (GEP) for the virtual gene Genetic Algorithm (vgGA) framework, and tested the co evolution of ADFs in three symbolic regression problems, comparing it with a conventional genetic algorithm. Our results show that on a simple function a conventional genetic algorithm performs better than our co evolutionary approach, but on a more complex functions the conventional genetic algorithm is outperformed by our co evolutionary approach. Also, we present an algorithm to implement GEP in a minimally invasive way in almost any genetic algorithm implementation.",
• notes = "Also known as \cite{6387221}",

}

Genetic Programming entries for Alejandro Sosa-Ascencio Manuel Valenzuela-Rendon Hugo Terashima-Marin

Citations