PVM: a framework for parallel distributed computing
Concurrency: Practice and Experience
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
A parallel genetic algorithm for performance-driven VLSI routing
IEEE Transactions on Evolutionary Computation
A circuit representation technique for automated circuit design
IEEE Transactions on Evolutionary Computation
Coevolutionary augmented Lagrangian methods for constrainedoptimization
IEEE Transactions on Evolutionary Computation
Markov chain models of parallel genetic algorithms
IEEE Transactions on Evolutionary Computation
A population-based hybrid extremal optimization algorithm
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16,17], and finds a better solution to G10 than [17].