Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
Parallel Implementation of EDAs Based on Probabilistic Graphical Models
IEEE Transactions on Evolutionary Computation
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This paper presents a parallel probabilistic model-building genetic algorithms (PMBGAs) called cellular compact genetic algorithm (CCGA) with elitism. The elitismbased CCCA is a coarse-grained parallel GA that migrates probability model between nodes instead of individuals. Each CCGA node is enhanced from compact genetic algorithm by using elitism. With elitism and our parallelized approach, the performance of the proposed parallel genetic algorithm is improved. The benchmarks and experimental results presented in the this paper confirm the performance of the proposed algorithm.