Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
Heterogeneous computing and parallel genetic algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Evolutionary computation: a unified approach
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Cellular Genetic Algorithms
Effects of scale-free and small-world topologies on binary coded self-adaptive CEA
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
A dynamic Island-based genetic algorithms framework
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A dynamic island model for adaptive operator selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Autonomous local search algorithms with island representation
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A fuzzy evolutionary framework for combining ensembles
Applied Soft Computing
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In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different "enough" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual.