poster

The directional EDA for global optimization

Authors:

Pedro P. Mayorga-Alvarez,

Arturo Hernández-AguirreAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 473 - 474

https://doi.org/10.1145/1389095.1389184

Published: 12 July 2008 Publication History

Get Access

Abstract

This article presents a robust EDA for global optimization with real parameters. The approach is based on the linear combination of individuals of two populations. One is the current population P_t, from which a probability density model is created and a new population P_s is simulated. The new population P_t+1 is a linear combination of P_t and P_s. The linear combination factor involved is self-adaptive.

References

[1]

J. Grahl, P. A. N. Bosman, and F. Rothlauf. The correlation-triggered adaptive variance scaling IDEA. In GECCO '06: Proceedings of the 8th annual conference on Genetic and Evolutionary Computation, pages 397--404. ACM Press, 2006.

Digital Library

Google Scholar

[2]

Q. Lu and X. Yao. Clustering and learning gaussian distribution for continuos optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 35(2):195--204, May 2005.

Digital Library

Google Scholar

Index Terms

The directional EDA for global optimization
1. Applied computing
  1. Physical sciences and engineering
    1. Mathematics and statistics

Recommendations

Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems

This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all ...
DE/EDA: a new evolutionary algorithm for global optimization

Differential evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of distribution algorithm (...
Accelerated biogeography-based optimization with neighborhood search for optimization

Biogeography-based optimization (BBO) inherently lacks exploration capability that leads to slow convergence. To address this limitation, authors present a memetic algorithm (MA) named as aBBOmDE, which is a new variant of BBO. In aBBOmDE, the ...

Comments

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
113
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Index Terms

Recommendations

Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems

DE/EDA: a new evolutionary algorithm for global optimization

Accelerated biogeography-based optimization with neighborhood search for optimization

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations