Applying Parallelism to Improve Genetic Algorithm-based Design Optimization
نویسنده
چکیده
The abundance of powerful workstations makes course-grained parallelization an obvious enhancement to many optimization techniques, including genetic algorithms [Gol89, DM97]. While initial modifications have beenmade toGADO (Genetic Algorithm forDesignOptimization [Ras98, RHG97]), such changes have not been carefully analyzed for potential impacts on quality. More generally, parallelizationhas the potential to improveGAperformance through the use of alternativemodels of computation. Parallelism can certainly reduce the total elapsed clock-time for a solution, but as a change inmodel of computation (either real or simulated), it can change the number of simulator calls and even make new solutions achievable. The effects of parallelization on GADOwere investigated during my summer internship at the Center for Computational Design.
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