eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2010-11-24
10361
1
19
10.4230/DagSemProc.10361.1
article
10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms
Auger, Anne
Shapiro, Jonathan L.
Whitley, L. Darrell
Witt, Carsten
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol10361/DagSemProc.10361.1/DagSemProc.10361.1.pdf
Evolutionary algorithms
bio-inspired search heuristics
theoretical analysis
optimization time
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2010-11-24
10361
1
0
10.4230/DagSemProc.10361.2
article
2-bit Flip Mutation Elementary Fitness Landscapes
Langdon, William
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol10361/DagSemProc.10361.2/DagSemProc.10361.2.pdf
Genetic Algorithms
Genetic Programming
search
optimisation
graph theory
Laplacian
Hamming cube
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2010-11-24
10361
1
10
10.4230/DagSemProc.10361.3
article
Exploring the common concepts of adaptive MCMC and Covariance Matrix Adaptation schemes
Mueller, Christian Lorenz
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol10361/DagSemProc.10361.3/DagSemProc.10361.3.pdf
Adaptive MCMC
Gaussian Adaptation
CMA-ES
covari- ance matrix adaptation