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We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration scheme of which follows a SOM-like dynamics. We succesfully apply this approach to clustering in both VLSI-design and psychotherapy research. The advantages of the approach are shown which consist in a reduced communication overhead between the sub-populations preserving a non-vanishing information flow
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distri...
This book brings together the current state of-the-art research in Self Organizing Migrating Algorit...
We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration sc...
Migration of individuals allows a fruitful interaction between subpopulations in the island model, a...
During the developing phase of the new evolutionary algorithm (EA) or the analysis, several techniqu...
Migration of individuals allows a fruitful interaction between subpopulations in the island model, a...
Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinato...
In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for so...
Parallelization of an evolutionary algorithm takes the advantage of modular population division and ...
This paper represents the next step in the development of the recently proposed single objective met...
Abstract. While evolutionary algorithms (EAs) have many advantages, they have to evaluate a relative...
The paper deals with a new evolutionary algorithm - Differential Migration, and provides comparison ...
In most of the popular implementation of Parallel GAs the whole population is divided into a set of ...
Analysing large-scale data brings promises of new levels of scientific discovery and economic value. ...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distri...
This book brings together the current state of-the-art research in Self Organizing Migrating Algorit...
We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration sc...
Migration of individuals allows a fruitful interaction between subpopulations in the island model, a...
During the developing phase of the new evolutionary algorithm (EA) or the analysis, several techniqu...
Migration of individuals allows a fruitful interaction between subpopulations in the island model, a...
Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinato...
In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for so...
Parallelization of an evolutionary algorithm takes the advantage of modular population division and ...
This paper represents the next step in the development of the recently proposed single objective met...
Abstract. While evolutionary algorithms (EAs) have many advantages, they have to evaluate a relative...
The paper deals with a new evolutionary algorithm - Differential Migration, and provides comparison ...
In most of the popular implementation of Parallel GAs the whole population is divided into a set of ...
Analysing large-scale data brings promises of new levels of scientific discovery and economic value. ...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distri...
This book brings together the current state of-the-art research in Self Organizing Migrating Algorit...
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