Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15883
Title: An immune-genetic algorithm for introduction planning of new products
Authors: Wang, D
Fung, RYK
Ip, WH 
Keywords: Immune system
Genetic algorithms
Machine learning
Constrained optimization
Semi-infinity programming
Introduction planning of products
Issue Date: 2009
Source: Computers & industrial engineering, 2009, v. 56, no. 3, p. 902-917 How to cite?
Journal: Computers & industrial engineering 
Abstract: The introduction planning problem of new products can be described as a semi-infinite programming model with infinite constraints. To solve complex constrained optimization problems, a new immune-genetic algorithm is proposed in this paper. In this approach, first of all, some antigens are randomly generated for the production and training of antibodies. Then, an efficient immune system with the capability to recognize self- and non-self-antigens is supported by these trained antibodies. The resulting immune system is built into genetic algorithms, and they can be used to identify and repair the illegal and infeasible chromosomes during the genetic iterations. The recommended algorithm can improve the performance of genetic algorithms particularly in complex constrained optimization problems. It has been achieved satisfactory results from the new product introduction problems.
URI: http://hdl.handle.net/10397/15883
ISSN: 0360-8352
DOI: 10.1016/j.cie.2008.09.036
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