A two-level hierarchical EDA using conjugate priori

Bo Wang; Hua Xu; Yuan Yuan

doi:10.1145/2598394.2598470

A two-level hierarchical EDA using conjugate priori

Bo Wang, Hua Xu, Yuan Yuan

Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.

Original language	English
Title of host publication	GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
Publisher	Association for Computing Machinery
Pages	57-58
Number of pages	2
ISBN (Print)	9781450328814
DOIs	https://doi.org/10.1145/2598394.2598470
State	Published - 2014
Externally published	Yes
Event	16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion - Vancouver, BC, Canada Duration: 12 Jul 2014 → 16 Jul 2014

Publication series

Name	GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

Conference

Conference	16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Country/Territory	Canada
City	Vancouver, BC
Period	12/07/14 → 16/07/14

Keywords

Artificial intelligence
Combinatorial optimization
Empirical study

Access to Document

10.1145/2598394.2598470

Cite this

Wang, B., Xu, H., & Yuan, Y. (2014). A two-level hierarchical EDA using conjugate priori. In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 57-58). (GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/2598394.2598470

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title = "A two-level hierarchical EDA using conjugate priori",

abstract = "Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.",

keywords = "Artificial intelligence, Combinatorial optimization, Empirical study",

author = "Bo Wang and Hua Xu and Yuan Yuan",

year = "2014",

doi = "10.1145/2598394.2598470",

language = "英语",

isbn = "9781450328814",

series = "GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery",

pages = "57--58",

booktitle = "GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference",

note = "16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion ; Conference date: 12-07-2014 Through 16-07-2014",

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Wang, B, Xu, H & Yuan, Y 2014, A two-level hierarchical EDA using conjugate priori. in GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, pp. 57-58, 16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion, Vancouver, BC, Canada, 12/07/14. https://doi.org/10.1145/2598394.2598470

A two-level hierarchical EDA using conjugate priori. / Wang, Bo; Xu, Hua; Yuan, Yuan.
GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2014. p. 57-58 (GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Bo

AU - Xu, Hua

AU - Yuan, Yuan

PY - 2014

Y1 - 2014

N2 - Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.

AB - Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.

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KW - Combinatorial optimization

KW - Empirical study

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PB - Association for Computing Machinery

T2 - 16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

Y2 - 12 July 2014 through 16 July 2014

ER -

A two-level hierarchical EDA using conjugate priori

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Keywords

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