Self-adjusting binary search trees
Journal of the ACM (JACM)
A data structure useful for finding Hamiltonian cycles
Theoretical Computer Science
Data structures for traveling salesmen
SODA '93 Selected papers from the fourth annual ACM SIAM symposium on Discrete algorithms
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
The Immune System as a Prototype of Autonomous Decentralized Systems: An Overview
ISADS '97 Proceedings of the 3rd International Symposium on Autonomous Decentralized Systems
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Negative selection based immune optimization
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
Multi-class iteratively refined negative selection classifier
Applied Soft Computing
A Novel Clonal Selection Algorithm and Its Application to Traveling Salesman Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Clonal selection algorithm with search space expansion scheme for global function optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A novel clonal selection for multi-modal function optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Applying the clonal selection principle to find flexible job-shop schedules
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
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Based on the clonal selection principle proposed by Burnet, in the immune response process there is no crossover of genetic material between members of the repertoire, i.e., there is no knowledge communication during different elite pools in the previous clonal selection models. As a result, the search performance of these models is ineffective. To solve this problem, inspired by the concept of the idiotypic network theory, an expanded lateral interactive clonal selection algorithm (LICS) is put forward. In LICS, an antibody is matured not only through the somatic hypermutation and the receptor editing from the B cell, but also through the stimuli from other antibodies. The stimuli is realized by memorizing some common gene segment on the idiotypes, based on which a lateral interactive receptor editing operator is also introduced. Then, LICS is applied to several benchmark instances of the traveling salesman problem. Simulation results show the efficiency and robustness of LICS when compared to other traditional algorithms.