|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.||URI:||http://hdl.handle.net/10553/121350|
|Appears in Collections:||Actas de congresos|
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