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Parallel Single and Multiple Objectives Genetic Algorithms: A Survey

Parallel Single and Multiple Objectives Genetic Algorithms: A Survey
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Author(s): B. S. P. Mishra (KIIT University, India), S. Dehuri (Fakir Mohan University, India), R. Mall (Indian Institute of Technology Kharagpur, India) and A. Ghosh (Indian Statistical Institute, India)
Copyright: 2011
Volume: 2
Issue: 2
Pages: 37
Source title: International Journal of Applied Evolutionary Computation (IJAEC)
Editor(s)-in-Chief: Wei-Chiang Samuelson Hong (Oriental Institute of Technology, Taiwan)
DOI: 10.4018/jaec.2011040102

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Abstract

This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution. However, some parallel single and multi-objective genetic algorithms converged to better solutions as compared to comparable sequential single and multiple objective genetic algorithms. The authors review several representative models for parallelizing single and multi-objective genetic algorithms. Further, some of the issues that have not yet been studied systematically are identified in the context of parallel single and parallel multi-objective genetic algorithms. Finally, some of the potential applications of parallel multi-objective GAs are discussed.

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