Title :
LEGA: an architecture for learning and evolving flexible job-shop schedules
Author :
Ho, Nhu Binh ; Tay, Joc Cing
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore
Abstract :
The interaction between evolution and learning has received much attention with recent studies in machine learning showing that it can significantly improve the efficiency of evolutionary strategies for job-shop scheduling. We propose a tripartite architecture called LEGA; comprising a population generator that improves the quality of the initial population for subsequent evolution while training a schemata learning module to modify the fitnesses of its offsprings aided by a memory of conserved schemas resolved from sampled schedules received dynamically during evolution. Experimental results indicate that an instantiation of LEGA outperforms current approaches using canonical EAs in computational time and quality of schedules.
Keywords :
evolutionary computation; flexible manufacturing systems; job shop scheduling; learning (artificial intelligence); optimisation; LEGA architecture; flexible job-shop schedule evolution; machine learning; population generator; schemata learning module; tripartite architecture; Customer satisfaction; Dynamic scheduling; Environmental economics; Evolutionary computation; Job shop scheduling; Machine learning; Manufacturing; Processor scheduling; Production; Space technology;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554851