A computationally efficient evolutionary algorithm for real-parameter optimization.
Evol Comput
; 10(4): 371-95, 2002.
Article
en En
| MEDLINE
| ID: mdl-12450456
ABSTRACT
Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we call the G3 model). The performance of the G3 model with the PCX operator is investigated on three commonly used test problems and is compared with a number of evolutionary and classical optimization algorithms including other real-parameter GAs with the unimodal normal distribution crossover (UNDX) and the simplex crossover (SPX) operators, the correlated self-adaptive evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), the differential evolution technique, and the quasi-Newton method. The proposed approach is found to consistently and reliably perform better than all other methods used in the study. A scale-up study with problem sizes up to 500 variables shows a polynomial computational complexity of the proposed approach. This extensive study clearly demonstrates the power of the proposed technique in tackling real-parameter optimization problems.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Evolución Biológica
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Evol Comput
Asunto de la revista:
BIOLOGIA
Año:
2002
Tipo del documento:
Article
País de afiliación:
India