Multimapping Chaotic Mind Evaluation Algorithm

Author

Liu, Jianxia ; Dai, Minmin ; Xie, Keming

Author_Institution

Coll. of Inf. Eng., Taiyuan Univ. of Technol., Taiyuan

fYear

2009

fDate

23-24 May 2009

Firstpage

1

Lastpage

4

Abstract

In order to overcome the disadvantages of Simple Mind Evaluation Algorithm (SMEA), such as the generation of the initial population is random and redundant, MEA and chaos are hybridized to form Multimapping Chaotic MEA (MCMEA), which reasonably combines the population-based evolutionary searching ability of MEA and chaotic searching behavior. In this method, two different chaos-mapping optimizations are introduced in different phases of population evolution. The chaotic ergodicity guides the evaluation to reach global optimum or its good approximation with high probability. The character of memory and optimum solution of the present generation are used to instruct the chaos search to improve searching efficiency. The test case confirmed the effectiveness, the flexibility and suitability of the proposed MCMEA.

Keywords

chaos; evolutionary computation; optimisation; chaos-mapping optimizations; chaotic ergodicity; chaotic searching behavior; multimapping chaotic mind evaluation algorithm; population evolution; population-based evolutionary searching ability; simple mind evaluation algorithm; Chaos; Character generation; Convergence; Educational institutions; Evolution (biology); Humans; Hybrid power systems; Nonlinear dynamical systems; Optimization methods; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-3893-8

Electronic_ISBN

978-1-4244-3894-5

Type

conf

DOI

10.1109/IWISA.2009.5072966

Filename

5072966