Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Genetic Algorithms and the Immune System
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
GA-easy and GA-hard Constraint Satisfaction Problems
Constraint Processing, Selected Papers
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
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Artificial Immune System algorithms use antibodies which fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm can not make use of schemata or classes of partial solutions. This paper presents a symbiotic artificial immune system (SymbAIS) algorithm which is an extension of CLONALG algorithm. It uses partially specified antibodies and gradually builds up building blocks of suitable sub-antibodies. The algorithm is compared with CLONALG on multimodal function optimization and combinatorial optimization problems and it is shown that it can solve problems that CLONALG is unable to solve.