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
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
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
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
Computing longest common subsequences with the B-cell algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Mutation rate matters even when optimizing monotonic functions
Evolutionary Computation
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 5.23 |
Artificial immune systems can be applied to a variety of very different tasks including function optimization. There are even artificial immune systems tailored specifically for this task. In spite of their successful application there is little knowledge and hardly any theoretical investigation about how and why they perform well. Here rigorous analyses for a specific class of mutation operators introduced for function optimization called somatic contiguous hypermutation is presented. Different concrete instantiations of this operator are considered and shown to behave quite differently in general. While there are serious limitations to the performance of this type of 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.