Title :
Multi-apprentice learning for meta-heuristics parameter tuning in a Multi Agent Scheduling System
Author :
Pereira, I. ; Madureira, A. ; de Moura Oliveira, P.
Author_Institution :
GECAD - Knowledge Eng. & Decision Support Group, Polytech. of Porto, Porto, Portugal
Abstract :
The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed 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.
Keywords :
case-based reasoning; combinatorial mathematics; learning (artificial intelligence); multi-agent systems; optimisation; scheduling; CBR module performance; NP hard combinatorial optimization problem; case based reasoning module; complexity theory; metaheuristics parameter tuning; multiagent scheduling system; multiapprentice learning; scheduling problem; Biology; Cognition; Processor scheduling; Schedules; Scheduling; Search problems; Tuning; case-based reasoning; learning; meta-heuristics; parameter tuning; scheduling;
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2012 Fourth World Congress on
Conference_Location :
Mexico City
Print_ISBN :
978-1-4673-4767-9
DOI :
10.1109/NaBIC.2012.6402236
