On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
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
A better approximation ratio for the vertex cover problem
ACM Transactions on Algorithms (TALG)
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
Approximating covering problems by randomized search heuristics using multi-objective models*
Evolutionary Computation
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
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
Approximating vertex cover using edge-based representations
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
How the (1+λ) evolutionary algorithm optimizes linear functions
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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
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The runtime of the immune inspired B-Cell Algorithm (BCA) for the NP-hard vertex cover problem is analysed. It is the first theoretical analysis of a nature-inspired heuristic as used in practical applications for a realistic problem. Since the performance of BCA in combinatorial optimisation strongly depends on the representation an encoding heuristic is used. The BCA outperforms mutation-based evolutionary algorithms (EAs) on instance classes that are known to be hard for randomised search heuristics (RSHs). With respect to average runtime, it even outperforms a crossover-based EA on an instance class previously used to show good performance of crossover. These results are achieved by the BCA without needing a population. This shows contiguous somatic hypermutation as an alternative to crossover without having to control population size and diversity. However, it is also proved that populations are necessary for the BCA to avoid arbitrarily bad worst case approximation ratios.