Abstract
Artificial Immune Systems have become the subject of great interest due to their powerful information processing capabilities. This is because the immune system has some salient features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibodies. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems such as recognition, computer security, optimization, etc. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing immunological features so as to maintain its diversity for the sake of solving the problems with multimodal fitness landscapes. We empirically show the effectiveness of our approach by applying the algorithm to the artificial ant problem.
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Hasegawa, Y., Iba, H. (2004). Multimodal Search with Immune Based Genetic Programming. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_27
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DOI: https://doi.org/10.1007/978-3-540-30220-9_27
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