An algorithm for solving the job-shop problem
Management Science
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Proceedings of the 5th International Conference on Genetic Algorithms
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
Architecture for an Artificial Immune System
Evolutionary Computation
Investigating a hybrid metaheuristic for job shop rescheduling
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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Gene libraries have been added to Artificial Immune Systems in analogy to biological immune systems, but to date no careful study of their effect has been made. This work investigates the contribution of gene libraries to Artificial Immune Systems by reproducing and extending an earlier system that used gene libraries. Performance on a job-shop scheduling problem is evaluated empirically with and without gene libraries, and with many different library configurations. We propose that gene libraries encourage diversity in a population of solutions and that the number of components in the gene library parameterises this effect. The number of gene libraries used is found to affect solution fitness and indeed using larger numbers of libraries (and therefore libraries of smaller components) enables higher fitness to be attained. We conclude that gene libraries are likely to be of use in applications where there is a need to maintain the diversity of solutions.