On the Analysis of Dynamic Restart Strategies for Evolutionary Algorithms

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.

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