Volume

Dagstuhl Seminar Proceedings, Volume 10361

Publication Details

  • published at: 2010-11-24
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.10361.1
10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms

Authors: Anne Auger, Jonathan L. Shapiro, L. Darrell Whitley, and Carsten Witt

Abstract
From September 5 to 10, the Dagstuhl Seminar 10361 ``Theory of Evolutionary Algorithms '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general.
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Anne Auger, Jonathan L. Shapiro, L. Darrell Whitley, and Carsten Witt. 10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 10361, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{auger_et_al:DagSemProc.10361.1,
  author =	{Auger, Anne and Shapiro, Jonathan L. and Whitley, L. Darrell and Witt, Carsten},
  title =	{{10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--19},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10361},
  editor =	{Anne Auger and Jonathan L. Shapiro and L. Darrell Whitley and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10361.1},
  URN =		{urn:nbn:de:0030-drops-28180},
  doi =		{10.4230/DagSemProc.10361.1},
  annote =	{Keywords: Evolutionary algorithms, bio-inspired search heuristics, theoretical analysis, optimization time}
}
Document
DOI: 10.4230/DagSemProc.10361.2
2-bit Flip Mutation Elementary Fitness Landscapes

Authors: William Langdon

Abstract
Genetic Programming parity is not elementary. GP parity cannot be represented as the sum of a small number of elementary landscapes. Statistics, including fitness distance correlation, of Parity's fitness landscape are calculated. Using Walsh analysis the eigen values and eigenvectors of the Laplacian of the two bit flip fitness landscape are given and a ruggedness measure for elementary landscapes is proposed. An elementary needle in a haystack (NIH) landscape is given.
Cite as

William Langdon. 2-bit Flip Mutation Elementary Fitness Landscapes. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 10361, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{langdon:DagSemProc.10361.2,
  author =	{Langdon, William},
  title =	{{2-bit Flip Mutation Elementary Fitness Landscapes}},
  booktitle =	{Theory of Evolutionary Algorithms},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10361},
  editor =	{Anne Auger and Jonathan L. Shapiro and L. Darrell Whitley and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10361.2},
  URN =		{urn:nbn:de:0030-drops-28146},
  doi =		{10.4230/DagSemProc.10361.2},
  annote =	{Keywords: Genetic Algorithms, Genetic Programming, search, optimisation, graph theory, Laplacian, Hamming cube}
}
Document
DOI: 10.4230/DagSemProc.10361.3
Exploring the common concepts of adaptive MCMC and Covariance Matrix Adaptation schemes

Authors: Christian Lorenz Mueller

Abstract
In the field of scientific modeling, one is often confronted with the task of drawing samples from a probability distribution that is only known up to a normalizing constant and for which no direct analytical method for sample generation is available. Since the past decade, adaptive Markov Chain Monte Carlo (MCMC) methods gained considerable attention in the statistics community in order to tackle this black-box (or indirect) sampling scenario. Common application domains are Bayesian statistics and statistical physics. Adaptive MCMC methods try to learn an optimal proposal distribution from previously accepted samples in order to efficiently explore the target distribution. Variable metric ap- proaches in black-box optimization, such as the Evolution Strategy with covariance matrix adaptation (CMA-ES) and Gaussian Adaption (GaA), use almost identical ideas to locate putative global optima. This extended abstract summarizes the common concepts in adaptive MCMC and co- variance matrix adaptation schemes. We also present how both types of methods can be unified within the Gaussian Adaptation framework and propose a unification of both fields as “grand challenge” for future research.
Cite as

Christian Lorenz Mueller. Exploring the common concepts of adaptive MCMC and Covariance Matrix Adaptation schemes. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 10361, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{mueller:DagSemProc.10361.3,
  author =	{Mueller, Christian Lorenz},
  title =	{{Exploring the common concepts of adaptive MCMC and Covariance Matrix Adaptation schemes}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10361},
  editor =	{Anne Auger and Jonathan L. Shapiro and L. Darrell Whitley and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.10361.3},
  URN =		{urn:nbn:de:0030-drops-28135},
  doi =		{10.4230/DagSemProc.10361.3},
  annote =	{Keywords: Adaptive MCMC, Gaussian Adaptation, CMA-ES, covari- ance matrix adaptation}
}

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  • Authors
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  • Auger, Anne
  • L
  • Langdon, William
  • M
  • Mueller, Christian Lorenz
  • S
  • Shapiro, Jonathan L.
  • W
  • Whitley, L. Darrell
  • Witt, Carsten

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