On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Effects of diversity control in single-objective and multi-objective genetic algorithms
Journal of Heuristics
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
How comma selection helps with the escape from local optima
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Speeding up evolutionary algorithms through restricted mutation operators
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
IEEE Transactions on Evolutionary Computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the choice of the parent population size*
Evolutionary Computation
Comparison of simple diversity mechanisms on plateau functions
Theoretical Computer Science
A genetic algorithm that adaptively mutates and never revisits
IEEE Transactions on Evolutionary Computation
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
A new memory based variable-length encoding genetic algorithm for multiobjective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A variance decomposition approach to the analysis of genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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It is widely assumed and observed in experiments that the use of diversity mechanisms in evolutionary algorithms may have a great impact on its running time. Up to now there is no rigorous analysis pointing out the use of different mechanisms with respect to the runtime behavior. We consider evolutionary algorithms that differ from each other in the way they ensure diversity and point out situations where the right mechanism is crucial for the success of the algorithm. The algorithms considered either diversify the population with respect to the search points or with respect to function values. Investigating simple plateau functions, we show that using the "right" diversity strategy makes the difference between an exponential and a polynomial runtime.