Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Scalable Parallel Programming with CUDA
Queue - GPU Computing
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
Genetic Programming and Evolvable Machines
Theoretical and Empirical Analysis of a GPU Based Parallel Bayesian Optimization Algorithm
PDCAT '09 Proceedings of the 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies
Many-threaded implementation of differential evolution for the CUDA platform
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
Information Sciences: an International Journal
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Parallelization is a straightforward approach to enhance the efficiency for evolutionary computation due to its inherently parallel nature. Since NVIDIA released the compute unified device architecture (CUDA), graphic processing units have enabled lots of scalable parallel programs in a wide range of fields. However, parallelization of model building for EDAs is rarely studied. In this paper, we propose two implementations on CUDA to speed up the model building in the extended compact genetic algorithm (ECGA). The first implementation is algorithmically identical to original ECGA. Aiming at a greater speed boost, the second implementation modifies the model building. It slightly decreases the accuracy of models in exchange for more speedup. Empirically, the first implementation achieves a speedup of roughly 359 to the baseline on 500-bit trap problem with order 5, and the second implementation achieves a speedup of roughly 506 to the baseline on the same problem. Finally, both of our implementations scale up to 9,800-bit trap problem with order~5 on one single Tesla C2050 GPU card.