NDLI: Gaussian Adaptation as a unifying framework for continuous black-box optimization and adaptive Monte Carlo sampling

Content Provider	IEEE Xplore Digital Library
Author	Müller, C.L. Sbalzarini, I.F.
Copyright Year	2010
Description	Author affiliation: Institute of Theoretical Computer Science and the Swiss Institute of Bioinformatics, ETH Zurich, CH-8092 Zürich, Switzerland (Müller, C.L.; Sbalzarini, I.F.)
Abstract	We present a unifying framework for continuous optimization and sampling. This framework is based on Gaussian Adaptation (GaA), a search heuristic developed in the late 1960's. It is a maximum-entropy method that shares several features with the (1+1)-variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The algorithm samples single candidate solutions from a multivariate normal distribution and continuously adapts the first and second moments. We present modifications that turn the algorithm into both a robust continuous black-box optimizer and, alternatively, an adaptive Random Walk Monte Carlo sampler. In black-box optimization, sample-point selection is controlled by a monotonically decreasing, fitness-dependent acceptance threshold. We provide general strategy parameter settings, stopping criteria, and restart mechanisms that render GaA quasi parameter free. We also introduce Metropolis GaA (M-GaA), where sample-point selection is based on the Metropolis acceptance criterion. This turns GaA into a Monte Carlo sampler that is conceptually similar to the seminal Adaptive Proposal (AP) algorithm. We evaluate the performance of Restart GaA on the CEC 2005 benchmark suite. Moreover, we compare the efficacy of M-GaA to that of the Metropolis-Hastings and AP algorithms on selected target distributions.
Starting Page	1
Ending Page	8
File Size	954004
Page Count	8
File Format	PDF
ISBN	9781424469093
e-ISBN	9781424469116
DOI	10.1109/CEC.2010.5586491
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Covariance matrix Benchmark testing Gaussian distribution Convergence Proposals Monte Carlo methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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