Randomized algorithms
Introduction to algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adjacency list matchings: an ideal genotype for cycle covers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Speeding up evolutionary algorithms through asymmetric mutation operators
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Biased mutation operators for subgraph-selection problems
IEEE Transactions on Evolutionary Computation
General lower bounds for the running time of evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Evolutionary algorithms are general randomized search heuristics and typically perform an unbiased random search that is guided only by the fitness of the search points encountered. However, in applications there is often problem-specific knowledge that suggests some additional bias. The use of appropriately biased variation operators may speed up the search considerably. Problems defined over bit strings of finite length often have the property that good solutions have only very few 1-bits or very few 0-bits. A mutation operator tailored toward such situations is studied under different perspectives and in a rigorous way discussing its assets and drawbacks. We consider the runtime of evolutionary algorithms using biased mutations on illustrative example functions as well as on function classes. A comparison with unbiased operators shows on which functions biased mutations lead to a speedup, on which functions biased mutations increase the runtime, and in which settings there is almost no difference in performance. The main focus is on theoretical runtime analysis yielding asymptotic results. These findings are accompanied by the results of empirical investigations that deliver additional insights.