|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 ï¬ve recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shufï¬ed 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.
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