Evolutionary computation with simulated annealing: conditions for optimal equilibrium distribution
In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary algorithms. The case of the Simulated Annealing algorithm for optimisation is considered as a simple evolution strategy with a control parameter allowing balance between the probability of obtaining an...
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2005
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9590 |
| Aporte de: |
| Sumario: | In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary algorithms. The case of the Simulated Annealing algorithm for optimisation is considered as a simple evolution strategy with a control parameter allowing balance between the probability of obtaining an optimal or near-optimal solution and the time that the algorithm will take to reach equilibrium. This capacity is analysed and a theoretical frame is presented, stating a general condition to be fulfilled by an evolutionary algorithm in order to ensure its convergence to a global maximum of the fitness function. |
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