Self-adaptation in evolving systems
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
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Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
AE '95 Selected Papers from the European conference on Artificial Evolution
How to Solve It: Modern Heuristics
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Evolving car designs using model-based automated safety analysis and optimisation techniques
Journal of Systems and Software - Special issue: Computer software & applications
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Introduction to the Special Issue: Self-Adaptation
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
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Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
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Evolutionary Computation
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
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IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Self-adaptive mutations may lead to premature convergence
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In stochastic optimisation, all currently employed algorithms have to be parameterised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of parameter control, i.e. the dynamic and automated variation of values for parameters used in approximate algorithms. The method uses an evaluation of the recent performance of previously applied parameter values and predicts how likely each of the parameter values is to produce optimal outcomes in the next cycle of the algorithm. The resulting probability distribution is used to determine the parameter values for the following cycle. The results of our experiments show a consistently superior performance of two very different EA algorithms when they are parameterised using the predictive parameter control method.