Parallel chaos immune evolutionary programming

Authors:
Cheng Bo;Guo Zhenyu;Bai Zhifeng;Cao Binggang
Affiliations:
Research & Development Center of Electric Vehicle Xi'an Jiaotong, University, Xi'an Jiaotong University, Xi'an, China;Research & Development Center of Electric Vehicle Xi'an Jiaotong, University, Xi'an Jiaotong University, Xi'an, China;Research & Development Center of Electric Vehicle Xi'an Jiaotong, University, Xi'an Jiaotong University, Xi'an, China;Research & Development Center of Electric Vehicle Xi'an Jiaotong, University, Xi'an Jiaotong University, Xi'an, China
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 3
Cited 1

Evolution and Optimum Seeking: The Sixth Generation

Evolution and Optimum Seeking: The Sixth Generation
Immunity Clonal Strategies

ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation

Parallel Chaos Search Based Incremental Extreme Learning Machine

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on Clonal Selection Theory, Parallel Chaos Immune Evolutionary Programming (PCIEP) is presented. On the grounds of antigen-antibody affinity, the original antibody population can be divided into two subgroups. Correspondingly, two new immune operators, Chaotic Clonal Operator (CCO) and Super Mutation Operator (SMO), are proposed. The former has strong search ability in the small space while the latter has better search ability in large space. Thus, combination searching local optimum with maintaining population diversity can be actualized by concurrently operating CCO and SMO. Compared with the Classical Evolutionary Programming (CEP) and Evolutionary Algorithms with Chaotic Mutations (EACM), experimental results show that PCIEP is of high efficiency and can effectively prevent premature convergence. Therefore, it can be employed to solve complicated machine learning problems.