A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization

Author

Matsumura, Yoshiyuki ; Kobayashi, Akihiro ; Sugiyama, Kiyotaka ; Pataky, Todd ; Yasuda, Toshiyuki ; Ohkura, Kazuhiro ; Sellers, Bill

Author_Institution

Shinshu Univ., Nagano, Japan

fYear

2013

fDate

13-13 July 2013

Firstpage

89

Lastpage

93

Abstract

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur´s gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase.

Keywords

biology; evolutionary computation; particle swarm optimisation; (μ,λ) evolutionary algorithm; dinosaur gait generation problem; dinosaur gait optimization; numerical optimization problems; particle swarm algorithm; Dinosaurs; Muscles; Optimization; Particle swarm optimization; Sociology; Statistics; (μ, λ) evolutionary algorithms and particle swarm optimization; A hybrid evolutionary algorithm; dinosaur´s gait generation problem;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence & Applications (IWCIA), 2013 IEEE Sixth International Workshop on

Conference_Location

Hiroshima

ISSN

1883-3977

Print_ISBN

978-1-4673-5725-8

Type

conf

DOI

10.1109/IWCIA.2013.6624791

Filename

6624791