Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Parametric Modelling of a Flexible Plate Structure Using Artificial Immune System Algorithm
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
A parallel coevolutionary immune neural network and its application to signal simulation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple local optima in its solution space. When using conventional nonlinear programming methods to solve a GGP problem, local optimum may be found, or the procedure may be mathematically tedious. To find the global optimum of a GGP problem, a bio-immune-based approach is considered. This study presents an artificial immune system (AIS) including: an operator to control the number of antigen-specific antibodies based on an idiotypic network hypothesis; an editing operator of receptor with a Cauchy distributed random number, and a bone marrow operator used to generate diverse antibodies. The AIS method was tested with a set of published GGP problems, and their solutions were compared with the known global GGP solutions. The testing results show that the proposed approach potentially converges to the global solutions.