Randomized algorithms
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Introduction to algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Ant Colony Optimization
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
A genetic algorithm for the longest common subsequence problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
Analysis of Evolutionary Algorithms for the Longest Common Subsequence Problem
Algorithmica - Including a Special Section on Genetic and Evolutionary Computation; Guest Editors: Benjamin Doerr, Frank Neumann and Ingo Wegener
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
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
An improved algorithm for the longest common subsequence problem
Computers and Operations Research
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
Starting from scratch: growing longest common subsequences with evolution
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A hyper-heuristic for the Longest Common Subsequence problem
Computational Biology and Chemistry
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 15th annual conference companion on Genetic and evolutionary computation
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Computing a longest common subsequence of a number of strings is a classical combinatorial optimisation problem with many applications in computer science and bioinformatics. It is a hard problem in the general case so that the use of heuristics is motivated. Evolutionary algorithms have been reported to be successful heuristics in practice but a theoretical analysis has proven that a large class of evolutionary algorithms using mutation and crossover fail to solve and even approximate the problem efficiently. This was done using hard instances. We reconsider the very same hard instances and prove that the B-cell algorithm outperforms these evolutionary algorithms by far. The advantage stems from the use of contiguous hypermutations. The result is another demonstration that relatively simple artificial immune systems can excel over more complex evolutionary algorithms in the domain of optimisation.