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GECCO
2007
Springer
150views Optimization» more  GECCO 2007»
15 years 3 months ago
Credit assignment in adaptive memetic algorithms
Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta...
J. E. Smith
GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
15 years 3 months ago
An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments
The success of evolutionary algorithms (EAs) depends crucially on finding suitable parameter settings. Doing this by hand is a very time consuming job without the guarantee to ...
Oliver Kramer, Bartek Gloger, Andreas Goebels
GECCO
2007
Springer
149views Optimization» more  GECCO 2007»
15 years 3 months ago
Informative performance metrics for dynamic optimisation problems
Existing metrics for dynamic optimisation are designed primarily to rate an algorithm’s overall performance. These metrics show whether one algorithm is better than another, but...
Stefan Bird, Xiaodong Li
GECCO
2007
Springer
149views Optimization» more  GECCO 2007»
15 years 3 months ago
Modeling XCS in class imbalances: population size and parameter settings
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the ...
Albert Orriols-Puig, David E. Goldberg, Kumara Sas...
GECCO
2007
Springer
191views Optimization» more  GECCO 2007»
15 years 3 months ago
Particle swarm guided evolution strategy
Evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics for tackling real-parameter optimization problems in evolutionary computa...
Chang-Tai Hsieh, Chih-Ming Chen, Ying-Ping Chen