Baldwinian learning in clonal selection algorithm for optimization

  • Authors:

  • Maoguo Gong;Licheng Jiao;Lining Zhang

  • Affiliations:

  • Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an 710 ...;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an 710 ...;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an 710 ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization.