The guilty net for the traveling salesman problem
Computers and Operations Research - Special issue on neural networks and operations research
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem
Neural Processing Letters
Multiobjective optimization using ideas from the clonal selection principle
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimal design using clonal selection algorithm
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
IEICE - Transactions on Information and Systems
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The Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. In the immune response, according to Burnet's clonal selection principle, the antigen imposes a selective pressure on the antibody population by allowing only those cells which specifically recognize the antigen to be selected for proliferation and differentiation. However ongoing investigations indicate that receptor editing, which refers to the process whereby antigen receptor engagement leads to a secondary somatic gene rearrangement event and alteration of the receptor specificity, is occasionally found in affinity maturation process. In this paper, we extend the traditional CSA approach by incorporating the receptor editing method, named RECSA, and applying it to the Traveling Salesman Problem. Thus, both somatic hypermutation (HM) of clonal selection theory and receptor editing (RE) are utilized to improve antibody affinity. Simulation results and comparisons with other general algorithms show that the RECSA algorithm can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of generations.