The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
A study on the extended unique input/output sequence
Information Sciences: an International Journal
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Unique input–output (UIO) sequences have important applications in conformance testing of finite state machines (FSMs). Previous experimental and theoretical research has shown that evolutionary algorithms (EAs) can compute UIOs efficiently on many FSM instance classes, but fail on others. However, it has been unclear how and to what degree EA parameter settings influence the runtime on the UIO problem. This paper investigates the choice of acceptance criterion in the (1 + 1) EA and the use of crossover in the $$(\mu+1)$$ Steady State Genetic Algorithm. It is rigorously proved that changing these parameters can reduce the runtime from exponential to polynomial for some instance classes of the UIO problem.