The time complexity of maximum matching by simulated annealing
Journal of the ACM (JACM)
Randomized algorithms
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Rigorous hitting times for binary mutations
Evolutionary Computation
Bounds for the convergence rate of randomized local search in a multiplayer load-balancing game
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
A comparison of simulated annealing with a simple evolutionary algorithm
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
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Randomized search heuristics like evolutionary algorithms and simulated annealing find many applications, especially in situations where no full information on the problem instance is available. In order to understand how these heuristics work, it is necessary to analyze their behavior on classes of functions. Such an analysis is performed here for the class of monotone pseudo-boolean polynomials. Results depending on the degree and the number of terms of the polynomial are obtained. The class of monotone polynomials is of special interest since simple functions of this kind can have an image set of exponential size, improvements can increase the Hamming distance to the optimum and, in order to find a better search point, it can be necessary to search within a large plateau of search points with the same fitness value.