Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Investigations in meta-GAs: panaceas or pipe dreams?
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Divide-and-Evolve: a new memetic scheme for domain-independent temporal planning
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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In this work, we explore the idea that parameter setting of stochastic metaheuristics should be considered as a multi-objective problem. The so-called "performance fronts" presented in this work are a collection of non-dominated parameters sets, satisfying both a speed and a precision objective. Experiments are conducted using a multi-objective evolutionary algorithm, in order to: (i) set a parameter of several continuous metaheuristics, and (ii) set parameters of an hybrid algorithm for temporal planning. Our results suggest that the performance fronts are well suited for setting the parameters of stochastic metaheuristics. The relative position, in the objective space, of several parameter fronts also permits to compare metaheuristics on a given problem. Moreover, this approach give insights on the algorithm behaviour.