Biologically influenced algorithms and parallelism in non-linear optimization
Biologically influenced algorithms and parallelism in non-linear optimization
Adaptive global optimization with local search
Adaptive global optimization with local search
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Explicit Parallelism of Genetic Algorithms through Population Structures
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Optimization Using Distributed Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
EXTENDING HPF FOR ADVANCED DATA PARALLEL APPLICATIONS
EXTENDING HPF FOR ADVANCED DATA PARALLEL APPLICATIONS
Software infrastructure for non-uniform scientific computations on parallel processors
ACM SIGAPP Applied Computing Review
Scalable Parallel Genetic Algorithms
Artificial Intelligence Review
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Multi-objective Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
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This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run time than synchronous GAs. Our analysis shows that this improvement is due to (1) decreased synchronization costs and (2) high numerical efficiency (e.g. fewer function evaluations) for the asynchronous GAs. This analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs.