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
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An Artificial Immune System for Evolving Amino Acid Clusters Tailored to Protein Function Prediction
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
| Hi-index | 0.00 |
When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.