Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Theoretical Computer Science
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
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Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
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Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
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Theory of Randomized Search Heuristics: Foundations and Recent Developments
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Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this work, we investigate a (1+1) Evolutionary Algorithm for optimizing functions over the space {0,...,r} n, where r is a positive integer. We show that for linear functions over {0,1,2}n, the expected runtime time of this algorithm is O(n log n). This result generalizes an existing result on pseudo-Boolean functions and is derived using drift analysis. We also show that for large values of r, no upper bound for the runtime of the (1+1) Evolutionary Algorithm for linear function on {0,...,r}n can be obtained with this approach nor with any other approach based on drift analysis with weight-independent linear potential functions.