Immune Memory and Gene Library Evolution in the Dynamic Clonal Selection Algorithm
Genetic Programming and Evolvable Machines
Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Gene libraries: coverage, efficiency and diversity
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
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Gene expression programming (GEP) is a recently developed evolutionary computation method for model learning and knowledge discovery. Sometimes it is not easy when use GEP to solve too complex problem, so enhancing the algorithm learning capability is necessary. This paper proposes an immune principle based GEP algorithm (iGEP), which combines gene library and clonal selection algorithm. The gene library is composed of subexpressions of GEP expression selected from the process of evolution. The proposed algorithm introduces some new features, including the best subexpression of GEP expression is selected as the solution of the problem, and some segments of gene library are used for hypermutation and receptor editing. In terms of convergence rate and computational efficiency, the experimented results on some benchmark problems of the UCI repository show that iGEP outperforms the standard GEP.