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GECCO
2005
Springer
143views Optimization» more  GECCO 2005»
14 years 1 months ago
Niching in evolution strategies
Evolutionary Algorithms (EAs), popular search methods for solving search and optimization problems, are known for successful and fast location of single optimal solutions. However,...
Ofer M. Shir, Thomas Bäck
GECCO
2006
Springer
123views Optimization» more  GECCO 2006»
13 years 11 months ago
Reconsidering the progress rate theory for evolution strategies in finite dimensions
This paper investigates the limits of the predictions based on the classical progress rate theory for Evolution Strategies. We explain on the sphere function why positive progress...
Anne Auger, Nikolaus Hansen
GECCO
2006
Springer
146views Optimization» more  GECCO 2006»
13 years 11 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
2011
IEEE
12 years 7 months ago
Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems
—Over the last two decades, many Differential Evolution (DE) strategies have been introduced for solving Optimization Problems. Due to the variability of the characteristics in o...
Saber M. Elsayed, Ruhul A. Sarker, Daryl Essam
CEC
2008
IEEE
14 years 2 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...