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Symbiotic artificial immune system

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Abstract

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.

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Authors and Affiliations

  1. Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    Ramin Halavati & Saeed Bagheri Shouraki

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  1. Ramin Halavati
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  2. Saeed Bagheri Shouraki
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Correspondence to Ramin Halavati.

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Halavati, R., Shouraki, S.B. Symbiotic artificial immune system. Soft Comput 13, 565–575 (2009). https://doi.org/10.1007/s00500-008-0316-x

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