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» Surrogate Constraint Functions for CMA Evolution Strategies
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KI
2009
Springer
13 years 11 months ago
Surrogate Constraint Functions for CMA Evolution Strategies
Many practical optimization problems are constrained black boxes. Covariance Matrix Adaptation Evolution Strategies (CMA-ES) belong to the most successful black box optimization me...
Oliver Kramer, André Barthelmes, Günte...
GECCO
2006
Springer
146views Optimization» more  GECCO 2006»
13 years 8 months ago
The dispersion metric and the CMA evolution strategy
An algorithm independent metric is introduced that measures the dispersion of a uniform random sample drawn from the top ranked percentiles of the search space. A low dispersion f...
Monte Lunacek, Darrell Whitley
CEC
2008
IEEE
13 years 11 months ago
Scalarization versus indicator-based selection in multi-objective CMA evolution strategies
Abstract—While scalarization approaches to multicriteria optimization become infeasible in the case of many objectives, for few objectives the benefits of populationbased method...
Thomas Voß, Nicola Beume, Günter Rudolp...
CEC
2008
IEEE
13 years 11 months ago
Natural Evolution Strategies
— This paper presents Natural Evolution Strategies (NES), a novel algorithm for performing real-valued ‘black box’ function optimization: optimizing an unknown objective func...
Daan Wierstra, Tom Schaul, Jan Peters, Jürgen...
GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
13 years 11 months ago
Performance analysis of niching algorithms based on derandomized-ES variants
A survey of niching algorithms, based on 5 variants of derandomized Evolution Strategies (ES), is introduced. This set of niching algorithms, ranging from the very first derandom...
Ofer M. Shir, Thomas Bäck