Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Global optimization
Measuring parallel processor performance
Communications of the ACM
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Heterogeneous computing and parallel genetic algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA)
Proceedings of the 5th International Conference on Genetic Algorithms
Hybrid Distributed Real-Coded Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On Modelling Evolutionary Algorithm Implementations through Co-operating Populations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Strategy Adaption by Competing Subpopulations
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An Adaptive Parallel Genetic Algorithm for VLSI-Layout Optimization
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Partnering Strategies For Fitness Evaluation In A Pyramidal Evolutionary Algrorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
.NET as a Platform for Implementing Concurrent Objects (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Parallel Heterogeneous Genetic Algorithms for Continuous Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parameter optimization in 3D reconstruction on a large scale grid
Parallel Computing
Computers and Operations Research
A meta-parallel evolutionary system for solving optimization problems
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Integration of genetic algorithm and cultural algorithms for constrained optimization
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Information Sciences: an International Journal
Dealing with hardware heterogeneity: a new parallel search model
Natural Computing: an international journal
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In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally provides a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. Here, we encompass the actual parallelization of the technique, and get deeper into the importance of running a synchronous versus an asynchronous version of the basic GD-RCGA model. We also present the evaluation of the parallel execution of GD-RCGA over two local area networks, a Fast-Ethernet network and a Myrinet network. Our results indicate that the GD-RCGA model maintains a very high level of accuracy for continuous optimization when run in parallel, and we also demonstrate the relative advantages of each algorithm version over the two networks. Finally, we show that the async parallelization scales better than the sync one, what suggests future research lines for WAN execution and new models of search based on the original two-faced cube.