Evolutionary multiobjective optimization: principles, procedures, and practices

Deb, Kalyanmoy (2010) Evolutionary multiobjective optimization: principles, procedures, and practices AIP Conference Proceedings, 1298 . pp. 12-17. ISSN 1551-7616

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Official URL: http://proceedings.aip.org/resource/2/apcpcs/1298/...

Related URL: http://dx.doi.org/10.1063/1.3516290


Multi-objective optimization problems deal with multiple conflicting objectives. In principle, they give rise to a set of trade-off Pareto-optimal solutions. Over the past one-and-half decade, evolutionary multi-objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, commercial softwares, numerous freely downloadable codes, a dedicated biannual conference running successfully five times so far since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. This is because evolutionary algorithms (EAs) work with a population of solutions and in solving multi-objective optimization problems, EAs can be modified to find and capture multiple solutions in a single simulation run. In this article, we make a brief outline of EMO principles, discuss one specific EMO algorithm, and present some current research issues of EMO.

Item Type:Article
Source:Copyright of this article belongs to American Institute of Physics.
Keywords:Algorithmic Languages; Functional Analysis; Research Initiatives
ID Code:81016
Deposited On:03 Feb 2012 11:45
Last Modified:03 Feb 2012 11:45

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