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» Entropy-Driven Parameter Control for Evolutionary Algorithms
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EUSFLAT
2007
150views Fuzzy Logic» more  EUSFLAT 2007»
13 years 9 months ago
Competitive Self-adaptation in Evolutionary Algorithms
Heuristic search for the global minimum is studied. This paper is focused on the adaptation of control parameters in differential evolution (DE) and in controlled random search (...
Josef Tvrdík, Ivan Krivý
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolutionary optimization of dynamic control problems accelerated by progressive step reduction
In this paper, we describe the use of an evolutionary algorithm (EA) to solve dynamic control optimization problems in engineering. In this class of problems, a set of control var...
Q. Tuan Pham
EMO
2003
Springer
93views Optimization» more  EMO 2003»
14 years 23 days ago
Intensity Modulated Beam Radiation Therapy Dose Optimization with Multiobjective Evolutionary Algorithms
We apply the NSGA-II algorithm and its controlled elitist version NSGA-IIc for the intensity modulated beam radiotherapy dose optimization problem. We compare the performance of th...
Michael Lahanas, Eduard Schreibmann, Natasa Milick...
GECCO
2009
Springer
126views Optimization» more  GECCO 2009»
14 years 5 days ago
Improving NSGA-II with an adaptive mutation operator
The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters pre...
Arthur Gonçalves Carvalho, Aluizio F. R. Ar...
ESOA
2006
13 years 11 months ago
Reinforcement Learning for Online Control of Evolutionary Algorithms
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). ...
A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Marti...