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» On the Evolution of Evolutionary Algorithms
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CEC
2007
IEEE
15 years 8 months ago
Parameter calibration using meta-algorithms
— Calibrating an evolutionary algorithm (EA) means finding the right values of algorithm parameters for a given problem. This issue is highly relevant, because it has a high imp...
W. A. de Landgraaf, A. E. Eiben, Volker Nannen
125
Voted
GECCO
2007
Springer
211views Optimization» more  GECCO 2007»
15 years 8 months ago
Multi-objective univariate marginal distribution optimisation of mixed analogue-digital signal circuits
Design for specific customer service plays a crucial role for the majority of the market in modern electronics. However, adaptability to an individual customer results in increasi...
Lyudmila Zinchenko, Matthias Radecker, Fabio Bisog...
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
15 years 7 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
110
Voted
GECCO
2004
Springer
125views Optimization» more  GECCO 2004»
15 years 7 months ago
Population-Based Iterated Local Search: Restricting Neighborhood Search by Crossover
Abstract. Iterated local search (ILS) is a powerful meta-heuristic algorithm applied to a large variety of combinatorial optimization problems. Contrary to evolutionary algorithms ...
Dirk Thierens
74
Voted
GECCO
2003
Springer
112views Optimization» more  GECCO 2003»
15 years 7 months ago
Dispersion-Based Population Initialization
Reliable execution and analysis of an evolutionary algorithm (EA) normally requires many runs to provide reasonable assurance that stochastic effects have been properly considered...
Ronald W. Morrison