Commenced in January 2007
Paper Count: 30172
Hybridizing Genetic Algorithm with Biased Chance Local Search
Abstract:This paper explores university course timetabling problem. There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very time-consuming fitness evaluation routines. Thus standard evolutionary algorithms lack of efficiency to deal with them. In this paper we have proposed a memetic algorithm that incorporates the problem specific knowledge such that most of chromosomes generated are decoded into feasible solutions. Generating vast amount of feasible chromosomes makes the progress of search process possible in a time efficient manner. Experimental results exhibit the advantages of the developed Hybrid Genetic Algorithm than the standard Genetic Algorithm.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059775Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1321
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