Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Journal of Computer Science and Technology
Some theoretical results about the computation time of evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
About the Time Complexity of Evolutionary Algorithms Based on Finite Search Space
Computational Intelligence and Security
About the Computation Time of Adaptive Evolutionary Algorithms
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Analysis of adaptive operator selection techniques on the royal road and long k-path problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-valued multimodal fitness landscape characterization for evolution
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
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The behavior of a (1+1)-ES process on Rudolph's binary long k paths is investigated extensively in the asymptotic framework with respect to string length l. First, the case of k=lα is addressed. For α⩾1/2, we prove that the long k path is a long path for the (1+1)-ES in the sense that the process follows the entire path with no shortcuts, resulting in an exponential expected convergence time. For α<1/2, the expected convergence time is also exponential, but some shortcuts occur in the meantime that speed up the process. Next, in the case of constant k, the statistical distribution of convergence time is calculated, and the influence of population size is investigated for different (μ+λ)-ES. The histogram of the first hitting time of the solution shows an anomalous peak close to zero, which corresponds to an exceptional set of events that speed up the expected convergence time with a factor of l2. A direct consequence of this exceptional set is that performing independent (1+1)-ES processes proves to be more advantageous than any population-based (μ+λ)-ES