| Reference : Evolutionary algorithms based on game theory and cellular automata with coalitions |
| Parts of books : Contribution to collective works | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/11314 | |||
| Evolutionary algorithms based on game theory and cellular automata with coalitions | |
| English | |
Dorronsoro, Bernabé  [University of lille 1 > LIFL] | |
| Burguillo, Juan Carlos [] | |
| Peleteiro, Ana [] | |
| Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| 2013 | |
| Handbook of Optimization | |
| Zelinka, I. | |
| Snasel, V. | |
| Abraham, A. | |
| Springer | |
| 481-503 | |
| Yes | |
| 978-3-642-30504-7 | |
| [en] Evolutionary algorithms ; Coallitions ; game theory | |
| [en] Cellular genetic algorithms (cGAs) are a kind of genetic algorithms
(GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems. | |
| University of Luxembourg: High Performance Computing - ULHPC | |
| Researchers | |
| http://hdl.handle.net/10993/11314 |
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