Title :
Optimal approximation of linear systems by an improved Clonal Selection Algorithm
Author :
Zhang, Lining ; Gong, Maoguo ; Jiao, Licheng ; Yang, Jie
Author_Institution :
Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an
Abstract :
Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.
Keywords :
approximation theory; learning (artificial intelligence); linear systems; optimisation; Baldwin clonal selection algorithm; Baldwin effect; Baldwinian learning operation; clonal proliferation operation; immunology; linear system; optimal approximation; Approximation algorithms; Evolutionary computation; Linear approximation; Linear systems;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4630847
