Add to ORKGIn 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.Facultad de Informátic
Many areas in which computational optimisation may be applied are multi-objective optimisation probl...
This thesis is available for Library use on the understanding that it is copyright material and that...
Randomized search heuristics like simulated annealing and evolutionary algorithms are applied succes...
In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary al...
In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary al...
This paper theoretically compares the performance of simulated annealing and evolutionary algorithms...
AbstractIn this paper we consider the extension of genetic algorithms (GAs) with a probabilistic Bol...
Simulated annealing is a useful heuristic for finding good solutions for difficult combinatorial opt...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
Simulated Annealing has proven to be a very sucessful heuristic for various combinatorial optimizati...
During the last three decades there has been a growing interest in algorithms which rely on analogie...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
In this paper we consider the extension of genetic algorithms (GAs) with a probabilistic Boltzmann r...
The guided random search techniques, genetic algorithms and simulated annealing, are very promising ...
Abstract. Randomized search heuristics like simulated annealing and evolutionary algorithms are appl...
Many areas in which computational optimisation may be applied are multi-objective optimisation probl...
This thesis is available for Library use on the understanding that it is copyright material and that...
Randomized search heuristics like simulated annealing and evolutionary algorithms are applied succes...
In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary al...
In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary al...
This paper theoretically compares the performance of simulated annealing and evolutionary algorithms...
AbstractIn this paper we consider the extension of genetic algorithms (GAs) with a probabilistic Bol...
Simulated annealing is a useful heuristic for finding good solutions for difficult combinatorial opt...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
Simulated Annealing has proven to be a very sucessful heuristic for various combinatorial optimizati...
During the last three decades there has been a growing interest in algorithms which rely on analogie...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
In this paper we consider the extension of genetic algorithms (GAs) with a probabilistic Boltzmann r...
The guided random search techniques, genetic algorithms and simulated annealing, are very promising ...
Abstract. Randomized search heuristics like simulated annealing and evolutionary algorithms are appl...
Many areas in which computational optimisation may be applied are multi-objective optimisation probl...
This thesis is available for Library use on the understanding that it is copyright material and that...
Randomized search heuristics like simulated annealing and evolutionary algorithms are applied succes...