Proceedings of the third international conference on Genetic algorithms
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
cAS: ant colony optimization with cunning ants
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comment on bio-inspired optimisation via GPU architecture: the genetic algorithm workload
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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This paper proposes methods of parallel evolutionary algorithms using multi-thread programming on a platform with multicore processors. For this study, we revise the previously proposed edge histogram sampling algorithm (EHBSA) which we call the enhanced EHBSA (eEHBSA). The parallelization models are designed using eEHBSA to increase the execution speed of the algorithm. We propose two types of parallel models; a synchronousmulti-thread model (SMTM), and an asyn-chronousmulti-thread model (AMTM). Experiments are performed using TSP. The results showed that both parallel methods increased the speed of the computation times nearly proportional to the number of cores for all test problems. The AMTM produced especially good run time results for small TSP instances without local search. A consideration on parallel evolutionary algorithms with many-core GPUs was also given for future work.