Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
The Impact of Global Structure on Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Tuning optimization algorithms for real-world problems by means of surrogate modeling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper, we show that sequential parameter optimization (SPO), a method that was designed for (offline) parameter tuning, can be successfully used as a controller for multistart approaches of evolutionary algorithms (EA). We demonstrate this by replacing the restart heuristic of the IPOP-CMA-ES with the SPO algorithm. Experiments on the BBOB 2010 test cases suggest that the performance is at least competitive while the approach provides more options, e.g. setting more than one parameter at once. Essentially, we argue that SPO is a generalization of the IPOP heuristic and that the distinction between tuning and control is---although often useful---an artificial one.