Model-based evolutionary algorithms
Dirk Thierens,Peter A. N. Bosman +1 more
- 06 Jul 2013
- pp 431-458
TL;DR: GECCO’16 Companion, July 20-24, 2016, Denver, CO, USA ACM 978-1-4503-4323-7/16/07.
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Abstract: Dirk Thierens Utrecht University Department of Information and Computing Sciences Utrecht, The Netherlands Peter A.N. Bosman Centrum Wiskunde & Informatica – CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands 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. Copyright is held by the owner/author(s). GECCO’16 Companion, July 20-24, 2016, Denver, CO, USA ACM 978-1-4503-4323-7/16/07. http://dx.doi.org/10.1145/2908961.2926975 Dirk Thierens & Peter A.N. Bosman. GECCO 2016 Tutorial Model-Based Evolutionary Algorithms. 01/110 Outline
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References
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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Pedro Larraanaga,Jose A. Lozano +1 more
- 01 Oct 2001
TL;DR: This book presents an introduction to Evolutionary Algorithms, a meta-language for programming with real-time implications, and some examples of how different types of algorithms can be tuned for different levels of integration.
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Towards a new evolutionary computation : advances in the estimation of distribution algorithms
José A. Lozano
- 01 Jan 2006
TL;DR: In this article, the authors link entropy to estimation of distribution algorithms and propose a parallel island model for the quadratic assignment problem and a hybrid Cooperative Search Evolutionary Algorithm.
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Scalable optimization via probabilistic modeling : from algorithms to applications
Martin Pelikan,Kumara Sastry,Erick Cantú-Paz +2 more
- 01 Jan 2006
TL;DR: The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle are used in the Extended Compact Genetic Algorithm (ECGA) as mentioned in this paper for linkage learning via Probabilistic Modeling.
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