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» Dynamic populations in genetic algorithms
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GECCO
2003
Springer
103views Optimization» more  GECCO 2003»
15 years 5 months ago
Are Multiple Runs of Genetic Algorithms Better than One?
Abstract. There are conflicting reports over whether multiple independent runs of genetic algorithms (GAs) with small populations can reach solutions of higher quality or can fin...
Erick Cantú-Paz, David E. Goldberg
119
Voted
NC
1998
138views Neural Networks» more  NC 1998»
15 years 1 months ago
Parallel Adaptive Genetic Algorithm
In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is exte...
Leo Budin, Marin Golub, Domagoj Jakobovic
95
Voted
ISCAS
2005
IEEE
179views Hardware» more  ISCAS 2005»
15 years 6 months ago
Robust stabilization of control systems using piecewise linear Lyapunov functions and evolutionary algorithm
— Piecewise linear Lyapunov functions are used to design control gain matrices so that closed systems are robust stable and attractive regions are expanded as large as possible i...
K. Tagawa, Y. Ohta
108
Voted
GECCO
2008
Springer
144views Optimization» more  GECCO 2008»
15 years 1 months ago
Maintaining diversity through adaptive selection, crossover and mutation
This paper presents an Adaptive Genetic Algorithm (AGA) where selection pressure, crossover and mutation probabilities are adapted according to population diversity statistics. Th...
Brian McGinley, Fearghal Morgan, Colm O'Riordan
85
Voted
GECCO
2000
Springer
114views Optimization» more  GECCO 2000»
15 years 4 months ago
Intelligent Recombination Using Individual Learning in a Collective Learning Genetic Algorithm
This paper introduces a new collective learning genetic algorithm (CLGA) which employs individual learning to do intelligent recombination based on a cooperative exchange of knowl...
Terry P. Riopka, Peter Bock