A guided tour of Chernoff bounds
Information Processing Letters
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
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Theoretical Computer Science
Theoretical advances in artificial immune systems
Theoretical Computer Science
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Multiobjective optimization using ideas from the clonal selection principle
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
A Markov chain model of the b-cell algorithm
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Maximal age in randomized search heuristics with aging
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
On the analysis of the simple genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Artificial Immune Systems (AIS) are an emerging new field of research in Computational Intelligence that are used in many areas of application, e. g. optimization, anomaly detection and classification. For optimization tasks usually hypermutation operators are used. In this paper, we show that the use of populations can be essential for the utility of such operators by analyzing the runtime of a simple population-based immune inspired algorithm on a classical example problem. The runtime bounds we prove are tight for the problem at hand. Moreover, we derive some general characteristics of the considered mutation operator as well as properties of the population, which hold for a class of pseudo-Boolean functions.