Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Programming: Artificial Nervous Systems, Artificial Embryos and Embryological Electronics
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Theoretical Investigation of a Parallel Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Parallel Genetic Programming on a Network of Transputers
Parallel Genetic Programming on a Network of Transputers
Information Sciences: an International Journal - Special issue: Evolutionary computation
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Voronoi strains: a spline path planning algorithm for complex environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Finding building blocks through eigenstructure adaptation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorithms (PGAs), particularly multi-population island-based models. Although the existence of multiple populations encourages population diversity, these problems are still difficult to solve. This paper introduces a new initialization mechanism for each of the populations of the islands based on Voronoi cells. In order to analyze the results, a series of different experiments using several real-value deceptive problems and a set of representative parameters (migration ratio, migration frequency and connectivity) have been chosen. The results obtained suggest that the Voronoi initialization method improves considerably the performance obtained with a traditionally uniform random initialization.