Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Parallel and distributed computing handbook
Parallel and distributed computing handbook
An introduction to distributed algorithms
An introduction to distributed algorithms
Competitive coevolutionary multi-agent systems: the application to mapping and scheduling problems
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
New trends in parallel and distributed evolutionary computing
Fundamenta Informaticae
Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Algorithms and Architectures
Parallel Algorithms and Architectures
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Loosely Coupled Distributed Genetic Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Coevolutionary Life-Time Learning
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation
Selected Papers from AISB Workshop on Evolutionary Computing
Expert Systems with Applications: An International Journal
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The problem of parallel and distributed function optimization is considered. Two coevolutionary algorithms with different degrees of parallelism and different levels of a global coordination are used for this purpose and compared with sequential genetic algorithm (GA). The first coevolutionary algorithm called a loosely coupled genetic algorithm (LCGA) represents a competitive coevolutionary approach to problem solving and is compared with another coevolutionary algoritm called cooperative coevolutionary genetic algorithm (CCGA). The algorithms are applied for parallel and distributed optimization of a number of test functions known in the area of evolutionary computation. We show that both coevolutionary algorithms outperform a sequential GA. While both LCGA and CCGA algorithms offer high quality solutions, they may compete to outperform each other in some specific test optimization problems. The LCGA may be recommended to be used in optimization systems when high degree of parallelism is possible and non global coordination is expected while the CCGA algorithm is useful when low degree of parallelism and global coordination is acceptable.