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|Title:||Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators|
|Keywords:||Immunological algorithms;Learning operators|
|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.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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