Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Architecture for an Artificial Immune System
Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
An interactive co-evolutionary CAD system for garment pattern design
Computer-Aided Design
Expert Systems with Applications: An International Journal
An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Expert Systems with Applications: An International Journal
Rapid pedestrian detection in unseen scenes
Neurocomputing
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An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivated by the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods, NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a population might lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA is designed to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimal solution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustrate the efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show that the algorithm is of a strong potential in solving practical problems.