Hybrid Distributed Real-Coded Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
ASPARAGOS, A Parallel Genetic Algorithm and Population Genetics
WOPPLOT '89 Workshop on Evolutionary Models and Strategies, Workshop on Parallel Processing: Logic, Organization, and Technology: Parallelism, Learning, Evolution
Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization
WOPPLOT '89 Workshop on Evolutionary Models and Strategies, Workshop on Parallel Processing: Logic, Organization, and Technology: Parallelism, Learning, Evolution
Cellular Genetic Algorithms
Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In this paper we evaluate 2 cellular genetic algorithms (CGAs), a single-population genetic algorithm, and a hill-climber on the Black Box Optimization Benchmarking testbed. CGAs are fine grain parallel genetic algorithms with a spatial structure imposed by embedding individuals in a connected graph. They are popular for their diversity-preserving properties and efficient implementations on parallel architectures. We find that a CGA with a uni-directional ring topology outperforms the canonical CGA that uses a bi-directional grid topology in nearly all cases. Our results also highlight the importance of carefully chosen genetic operators for finding precise solutions to optimization problems.