A guided tour of Chernoff bounds
Information Processing Letters
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Speeding up evolutionary algorithms through asymmetric mutation operators
Evolutionary Computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
A Markov chain model of the b-cell algorithm
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Comparing Different Aging Operators
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Optimal fixed and adaptive mutation rates for the leadingones problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
On benefits and drawbacks of aging strategies for randomized search heuristics
Theoretical Computer Science
On the analysis of the immune-inspired B-cell algorithm for the vertex cover problem
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Variation in artificial immune systems: hypermutations with mutation potential
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Artificial immune systems for optimisation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
Artificial Immune Systems (AIS) are an emerging new field of research in Computational Intelligence that are applied to many areas of application, e.g., optimization, anomaly detection and classification. For optimization tasks, the use of hypermutation operators constitutes a common concept in AIS. By now, only little theoretical work has been done in this field. In this paper, we present a detailed theoretical runtime analysis that gives an insight into the dynamics of fitness based hypermutation processes. Two specific mutation rates are considered using a simple immune inspired algorithm. Our main focus lies thereby on the influence of parameters embedded in popular immune inspired hypermutation operators from the literature. Our theoretical findings are accompanied by some empirical results.