Please use this identifier to cite or link to this item:
Title: Enhancing A Multiobjective Evolutionary Algorithm Through Flexible Evolution
Authors: Salazar, D
Galván González, Blas José 
Winter Althaus, Gabriel 
UNESCO Clasification: 12 Matemáticas
Keywords: Evolutionary Algorithms
Issue Date: 2004
Conference: Genetic and Evolutionary Computation Conference (GECCO-2004)
Abstract: In this paper the use of a powerful single-objective optimization methodology in Multi-objective Optimization Algorithms (MOEAs) is introduced. The Flexible Evolution concepts (FE) have been recently developed and proved its efficiency gains compared with several Evolutionary Algorithms solving single-objective challenging problems. The main feature of such concepts is the flexibility to self-adapt the internal behaviour of the algorithm to optimize its search capacity. In this paper we present the first attempt to incorporate FE into MOEAs. A real coded NSGA-II algorithm was modified replacing the crossover and mutation operators with the Sampling Engine of FE. Other two FE characteristics were implemented too: The Probabilistic Control Mechanism and the Enlarged Individual’s Code. The performance of the resulting algorithm has been compared with the classical NSGA-II using several test functions. The results obtained and presented show that FE_based algorithms have advantages over the classical ones, especially when optimizing highly multimodal complex functions.
Appears in Collections:Actas de congresos
Adobe PDF (423,25 kB)
Show full item record

Google ScholarTM



Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.