Autonomous Tuning for Constraint Programming Via Artificial Bee Colony Optimization

No Thumbnail Available

Date

2015

Journal Title

Journal ISSN

Volume Title

Publisher

Springer-verlag Berlin

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Organizational Unit
Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

Journal Issue

Abstract

Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.

Description

johnson, franklin/0000-0003-4522-3809; Misra, Sanjay/0000-0002-3556-9331; Crawford, Broderick/0000-0001-5500-0188; Galleguillos, Cristian/0000-0001-9460-8719

Keywords

Artificial intelligence, Optimization, Adaptive systems, Metaheuristics

Turkish CoHE Thesis Center URL

Fields of Science

Citation

2

WoS Q

Scopus Q

Source

15th International Conference on Computational Science and Its Applications (ICCSA) -- JUN 22-25, 2015 -- Banff, CANADA

Volume

9155

Issue

Start Page

159

End Page

171

Collections