Randomized algorithms
An overview of parameter control methods by self-adaption in evolutionary algorithms
Fundamenta Informaticae
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Selectively Destructive Re-start
Proceedings of the 6th International Conference on Genetic Algorithms
Restart Scheduling for Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Rigorous hitting times for binary mutations
Evolutionary Computation
On the local performance of simulated annealing and the (1+1) evolutionary algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
How randomized search heuristics find maximum cliques in planar graphs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Maximal age in randomized search heuristics with aging
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Comparing Different Aging Operators
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Age-fitness pareto optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On benefits and drawbacks of aging strategies for randomized search heuristics
Theoretical Computer Science
Multistart strategy using delta test for variable selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
Mega process genetic algorithm using grid MP
LSGRID'04 Proceedings of the First international conference on Life Science Grid
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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Since evolutionary algorithms make heavy use of randomness it is typically the case that they succeed only with some probability. In cases of failure often the algorithm is restarted. Of course, it is desirable that the point of time when the current run is considered to be a failure and therefore the algorithm is stopped and restarted is determined by the algorithm itself rather than by the user. Here, very simple restart strategies that are non-adaptive are compared on a number of examples with different properties. Circumstances under which specific types of dynamic restart strategies should be applied are described and the potential loss by choosing an inadequate restart strategy is estimated.