Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Parallel genetic algorithms for a hypercube
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
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
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
An Overview of Evolutionary Computation
ECML '93 Proceedings of the European Conference on Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Theoretical Investigation of a Parallel Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Parallel heterogeneous genetic algorithms for continuous optimization
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
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The purpose of the Meta-Parallel Evolutionary System (MPES) is to develop fast, efficient parallel evolutionary systems for function optimization. Given an optimization problem and a set number of nodes available for the computation, the MPES searches for a strong, potentially heterogeneous combination of evolutionary algorithms to coordinate in order to effectively solve a problem. The Evolutionary Algorithms that are utilized in the parallel system are a Particle Swarm Optimizer (PSO), a variety of Genetic Algorithms (GAs), and an Evolutionary Hill-Climber Algorithm (EHC). The subpopulations communicate with each other via one or more centralized buffers. At a higher level exists the MPES, which uses evolutionary methods in order to discover parameters for effective parallel systems. This methodology provides an immediate benefit in the form of a strong tool to solve the optimization problem. Further, it provides a long-term benefit by identifying a system that has the potential to effectively solve other difficult optimization problems with a similar search space.