Theory of evolutionary algorithms: a bird's eye view
Theoretical Computer Science - Special issue on evolutionary computation
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Metropolis and Symmetric Functions: A Swan Song
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
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This paper is concerned with the question to which extent a change in the selective pressure might improve the runtime of an optimization algorithm considerably. The subject of this examination is the class of symmetric functions, i.e. OneMax with a subsequent application of a real valued function. We consider an improvement in runtime as considerable if an exponential runtime becomes polynomial. The basis for this examination is a Markov chain analysis. An exact criterion for static selective pressure, telling which functions are solvable in polynomial time, is extended to a sufficient (but not necessary) criterion for changing selection pressure.