Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Recognition of handwritten indic script using clonal selection algorithm
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
The quaternion model of artificial immune response
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
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Immune-inspired optimization algorithms encoded the parameters into individuals where each individual represents a search point in the space of potential solutions. A large number of parameters would result in a large search space. Nowadays, there is little report about immune algorithms effectively solving numerical optimization problems with more than 100 parameters. In this paper, we introduce an improved immune algorithm, termed as Dual-Population Immune Algorithm (DPIA), to solve large-scale optimization problems. DPIA adopts two side-by-side populations, antibody population and memory population. The antibody population employs the cloning, affinity maturation, and selection operators, which emphasizes the global search. The memory population stores current representative antibodies and the update of the memory population pay more attention to maintain the population diversity. Normalized decimal-string representation makes DPIA more suitable for solving large-scale optimization problems. Special mutation and recombination methods are adopted to simulate the somatic mutation and receptor editing process. Experimental results on eight benchmark problems show that DPIA is effective to solve large-scale numerical optimization problems.