Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12333
Title: Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators
Authors: Gao, S
Gong, T
Zhong, W
Wang, F
Chen, B
Keywords: Immunological algorithms;Learning operators
Issue Date: 2013
Publisher: Artificial Immune Systems - ICARIS
Citation: 12th European Conference on Artificial Life (2013), pp. 876 - 883, Taormina, Italy, (2-6 September 2013)
Abstract: Immunological algorithms are a kind of bio-inspired intelligence methods which draw inspiration from natural immune systems. The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators. In this paper, nine different learning operators in a standard immune algorithmic framework are investigated. These learning operators consist of eight existing operators and a newly proposed search direction based operator. Experiments are conducted based on nine variants of immunological algorithms that use different learning operators. Simulation results on a large number of benchmark optimization problems give a deep insight into the characteristics of these operators, and further verify that the proposed new learning operator can greatly improve the performance of immunological algorithms.
URI: http://bura.brunel.ac.uk/handle/2438/12333
DOI: http://dx.doi.org/10.7551/978-0-262-31709-2-ch130
Appears in Collections:Dept of Computer Science Research Papers

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