Please use this identifier to cite or link to this item:
http://bura.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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 647.79 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.