A guided tour of Chernoff bounds
Information Processing Letters
An overview of parameter control methods by self-adaption in evolutionary algorithms
Fundamenta Informaticae
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
Rigorous hitting times for binary mutations
Evolutionary Computation
Theoretical Aspects of Evolutionary Algorithms
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
On the Expected Runtime and the Success Probability of Evolutionary Algorithms
WG '00 Proceedings of the 26th International Workshop on Graph-Theoretic Concepts in Computer Science
Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Optimal fixed and adaptive mutation rates for the leadingones problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimizing monotone functions can be difficult
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
Non-uniform mutation rates for problems with unknown solution lengths
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Mutation rates of the (1+1)-EA on pseudo-boolean functions of bounded epistasis
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Variation in artificial immune systems: hypermutations with mutation potential
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
A comparison of simulated annealing with a simple evolutionary algorithm
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Mutation rate matters even when optimizing monotonic functions
Evolutionary Computation
Fitness function distributions over generalized search neighborhoods in the q-ary hypercube
Evolutionary Computation
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When evolutionary algorithms are used for function optimization, they perform a heuristic search that is influenced by many parameters. Here, the choice of the mutation probability is investigated. It is shown for a non-trivial example function that the most recommended choice for the mutation probability 1/n is by far not optimal, i. e., it leads to a superpolynomial running time while another choice of the mutation probability leads to a search algorithm with expected polynomial running time. Furthermore, a simple evolutionary algorithm with an extremely simple dynamic mutation probability scheme is suggested to overcome the difficulty of finding a proper setting for the mutation probability.