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GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

Weihang Zhu, Yaohang Li
Department of Industrial Engineering, Lamar University, 211 Redbird Ln, P.O. Box 10032, Beaumont, TX 77710
In Proceeding of the 2nd workshop on Bio-inspired algorithms for distributed systems (2010), pp. 1-8

@conference{zhu2010gpu,

   title={GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space},

   author={Zhu, W. and Li, Y.},

   booktitle={Proceeding of the 2nd workshop on Bio-inspired algorithms for distributed systems},

   pages={1–8},

   year={2010},

   organization={ACM}

}

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In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto optimal front in the multi-objective function space. The computational results show the effectiveness of the DE-MCMC algorithm in a variety of standardized test functions as well as a protein loop structure sampling application. Moreover, the DE-MCMC algorithm can efficiently take advantage of the massive-parallel, many-core architecture, where its implementation on GPU can achieve speedup of 14~35.
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