An Analytical Study of Parallel GA with Independent Runs on GPUs

This chapter proposes a genetic algorithm for solving QAPs with parallel independent run using GPU computation and gives a statistical analysis on how speedup can be attained with this model. With the proposed model, we achieve a GPU computation performance that is nearly proportional to the number of equipped multiprocessors (MPs) in the GPUs. We explain these computational results by performing statistical analysis. Regarding performance comparison to CPU computations, GPU computation shows a speedup of 7.2× and 13.1× on average using a single GTX 285 GPU and two GTX 285 GPUs, respectively. The parallel independent run model is the simplest of the various parallel evolutionary computation models, and among the models it demonstrates the lower limit performance.

[1]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[2]  Zhong-Xian Chi,et al.  An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated , 2007, 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007).

[3]  Shigeyoshi Tsutsui.  Parallel Ant Colony Optimization for the Quadratic Assignment Problems with Symmetric Multi Processing , 2008, ANTS Conference.

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Tien-Tsin Wong,et al.  Evolutionary Computing on Consumer Graphics Hardware , 2007, IEEE Intelligent Systems.

[6]  Vittorio Maniezzo,et al.  The Ant System Applied to the Quadratic Assignment Problem , 1999, IEEE Trans. Knowl. Data Eng..

[7]  Shigeyoshi Tsutsui,et al.  Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study , 2009, GECCO '09.

[8]  Teofilo F. Gonzalez,et al.  P-Complete Approximation Problems , 1976, J. ACM.

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[11]  Franz Rendl,et al.  QAPLIB – A Quadratic Assignment Problem Library , 1997, J. Glob. Optim..

[12]  Nicolas Lachiche,et al.  Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA , 2009, GECCO.

[13]  David W. Corne,et al.  A Real Coded Genetic Algorithm with an Explorer and an Exploiter Populations , 1997, ICGA.

[14]  L. Darrell Whitley,et al.  Changing Representations During Search: A Comparative Study of Delta Coding , 1994, Evolutionary Computation.

[15]  Samir W. Mahfoud.  A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[16]  Darrell Whitley,et al.  Scheduling problems and traveling salesman: the genetic edge recombination , 1989 .

[17]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[18]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.