Discrete Optimization-Based on the Combined Use of Reinforcement and Constraint Satisfaction Schemes
Loading...
Date
Authors
Likas, A.
Kontoravdis, D.
Stafylopatis, A.
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Type
Type of the conference item
Journal type
peer reviewed
Educational material type
Conference Name
Journal name
Neural Computing & Applications
Book name
Book series
Book edition
Alternative title / Subtitle
Description
A new approach is presented for finding near-optimal solutions to discrete optimisation problems that is based on the cooperation of two modules: an optimisation module and a constraint satisfaction module, The optimisation module must be able to search the problem state space through an iterative process of sampling and evaluating the generated samples. To evaluate a generated point, first a constraint satisfaction module is employed to map that point to another one satisfying the problem constraints, and then the cost of the new point is used as the evaluation of the original one, The scheme that we have adopted for testing the effectiveness of the method uses a reinforcement learning algorithm in the optimisation module and a general deterministic constraint satisfaction algorithm in the constraint satisfaction module. Experiments using this scheme for the solution of two optimisation problems indicate that the proposed approach is very effective in providing feasible solutions of acceptable quality.
Description
Keywords
constraint satisfaction, discrete optimization, graph partitioning, higher-order hopfield, reinforcement learning, set partitioning, networks
Subject classification
Citation
Link
Language
en
Publishing department/division
Advisor name
Examining committee
General Description / Additional Comments
Institution and School/Department of submitter
Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής