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Runtime analysis of evolutionary algorithms: basic introduction

Published: 06 July 2018 Publication History
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References

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Tianshi Chen, Jun He, Guangzhong Sun, Guoliang Chen, and Xin Yao. A new approach for analyzing average time complexity of population-based evolutionary algorithms on unimodal problems. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(5):1092--1106, Oct. 2009. ISSN 1083-4419.
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Dogan Corus, Duc-Cuong Dang, Anton V. Eremeev, and Per Kristian Lehre. Level-based analysis of genetic algorithms and other search processes. IEEE Transactions on Evolutionary Computation, pages 1--1, 2017. ISSN 1089--778X.
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Duc-Cuong Dang and Per Kristian Lehre. Refined Upper Bounds on the Expected Runtime of Non-elitist Populations from Fitness-Levels. In Proceedings of the 16th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO 2014), pages 1367--1374, 2014. ISBN 9781450326629.
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Duc-Cuong Dang and Per Kristian Lehre. Efficient optimisation of noisy fitness functions with population-based evolutionary algorithms. In Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, FOGA '15, pages 62--68, New York, NY, USA, 2015. ACM. ISBN 978-1-4503-3434-1. URL
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Jun He and Xin Yao. Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence, 145(1-2):59--97, 2003.
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Thomas Jansen, Kenneth A. De Jong, and Ingo Wegener. On the choice of the offspring population size in evolutionary algorithms. Evolutionary Computation, 13(4):413--440, 2005.
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Per Kristian Lehre. Negative drift in populations. In Proceedings of Parallel Problem Solving from Nature - (PPSN XI), volume 6238 of LNCS, pages 244--253. Springer Berlin / Heidelberg, 2011a.
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Per Kristian Lehre. Fitness-levels for non-elitist populations. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, (GECCO 2011), pages 2075--2082, New York, NY, USA, 2011b. ACM. ISBN 978-1-4503-0557-0.
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Per Kristian Lehre and Xin Yao. On the impact of mutation-selection balance on the runtime of evolutionary algorithms. Evolutionary Computation, IEEE Transactions on, 16(2):225--241, April 2012. ISSN 1089-778X.
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Frank Neumann, Pietro Simone Oliveto, and Carsten Witt. Theoretical analysis of fitness-proportional selection: landscapes and efficiency. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO 2009), pages 835--842, New York, NY, USA, 2009. ACM. ISBN 978-1-60558-325-9.
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Pietro S. Oliveto and Carsten Witt. On the runtime analysis of the simple genetic algorithm. Theoretical Computer Science, 545:2--19, 2014.
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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 06 July 2018

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