Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Hints for Adaptive Problem Solving Gleaned from Immune Networks
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
MILA: multilevel immune learning algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
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
| Hi-index | 0.00 |
Artificial Immune algorithms are relatively new randomized meta-heuristics and not a lot of work has been done on parallel immune algorithms yet. Most of these implementations use some version of the first generation artificial immune algorithms. In this research a novel parallel artificial immune algorithm for optimization is proposed based on cutting edge research in the study of germinal center reaction. This parallelism of the algorithm is inherent in the system as a whole, which is different than other parallel implementations of nature inspired algorithms, where several instances of the algorithm is run multiple times to exploit parallel architecture of computers. This system is being developed with input from immunologist and incorporates new ideas which have not been explored before. Some preliminary results are presented which hint that it could perform better than the evolutionary algorithm ((1+1)EA), with which it is compared. The algorithm is not limited to optimization and in the future the research will look into other application areas. Also limitations, improvements and applications where it excels, will be explored in the research.