Evolutionary Methods Of Optimization

Global Journal of Technology and Optimization (GJTO) is area of Most viewed journals in evolutionary methods of optimization. This is a Journal of Global Optimization explicitly invites submissions dealing with (possibly unsolved) problems arising from applications. Short communications on open and solved global optimization problems will be published in a Problems Section. The journal will also publish reviews of appropriate articles and special issues of journals. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. OMICS Group through its Open Access Initiative is committed to make genuine and reliable contributions to the scientific community. OMICS Group hosts over 700+ peer-reviewed journals and has organised over 3000+ International Scientific Conferences all over the world. OMICS group support to the GJTO Journal Dealing with Theoretical and Computational Aspects of Seeking Global Optima and Their Applications in Science, Management and Engineering.
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Last date updated on June, 2014

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