Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
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 design of problem-specific evolutionary algorithms
Advances in evolutionary computing
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
On the utility of the population size for inversely fitness proportional mutation rates
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
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
Diophantine benchmarks for the b-cell algorithm
ICARIS'06 Proceedings of the 5th 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
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
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Artificial immune systems can be applied to a variety of very different tasks including classical function optimization. There are even artificial immune systems tailored specifically for this task. In spite of the successful application there is little knowledge and hardly any theoretical investigation about how and why they perform well. Here a rigorous analysis for a specific type of mutation operator introduced for function optimization called somatic contiguous hypermutation is presented. While there are serious limitations to the performance of this operator even for simple optimization tasks it is proven that for some types of optimization problems it performs much better than standard bit mutations most often used in evolutionary algorithms.