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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
13 years 10 months ago
Transition models as an incremental approach for problem solving in evolutionary algorithms
This paper proposes an incremental approach for building solutions using evolutionary computation. It presents a simple evolutionary model called a Transition model in which parti...
Anne Defaweux, Tom Lenaerts, Jano I. van Hemert, J...
GECCO
2004
Springer
131views Optimization» more  GECCO 2004»
13 years 10 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
FLAIRS
2003
13 years 6 months ago
Sample Complexity of Real-Coded Evolutionary Algorithms
Researchers studying Evolutionary Algorithms and their applications have always been confronted with the sample complexity problem. The relationship between population size and gl...
Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
GECCO
2007
Springer
164views Optimization» more  GECCO 2007»
13 years 11 months ago
A study of mutational robustness as the product of evolutionary computation
This paper investigates the ability of a tournament selection based genetic algorithm to find mutationally robust solutions to a simple combinatorial optimization problem. Two di...
Justin Schonfeld
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
13 years 11 months ago
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since mos...
Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendh...