Tuning meta-heuristics using multi-agent learning in a scheduling system

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86099416" target="_blank" >RIV/61989100:27240/13:86099416 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/13:86099416
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-45318-2-8" target="_blank" >http://dx.doi.org/10.1007/978-3-642-45318-2-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-45318-2-8" target="_blank" >10.1007/978-3-642-45318-2-8</a>

Result language
angličtina
Original language name
Tuning meta-heuristics using multi-agent learning in a scheduling system
Original language description
In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module in order to solve the parameter-tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance. (C) 2013 Springer-Verlag Berlin Heidelberg.
Czech name
—
Czech description
—

Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)