Local cover image
Local cover image
Local cover image
Local cover image

Comparison of static and dynamic penalty functions for handling constraints in genetic algorithm applied to course timetabling for CSM in UPMin / Erick Castillo Concepcion.

By: Material type: TextTextLanguage: English Publication details: 2006Description: 54 leavesSubject(s): Abstract: This study presents an application of generic algorithm (GA) on university course timetabling problem using penalty functions were utilized as fitness function, penalizing only on the soft constraints since all hard constraints must be satisfied. One is static penalty function having a fixed penalty parameter in the entire optimization process, and dynamic penalty function where the penalty factors are dependent on the current generation. In comparing the solution between the best for static penalty and the best for dynamic penalty, the later has relatively lower constraint violation making that solution better than the static penalty. Thus, dynamic penalty function is a better performer than static penalty function as a fitness function in a GA optimization process, although the soft constraints were partitioned into equality and inequality constraints, it is not clear which among the constraints is more violated because they have a different degree of penalty
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Cover image Item type Current library Collection Call number Status Date due Barcode
Thesis Thesis University Library Theses Room-Use Only LG993.5 2006 A64 C65 (Browse shelf(Opens below)) Not For Loan 3UPML00011759
Thesis Thesis University Library Archives and Records Preservation Copy LG993.5 2006 A64 C65 (Browse shelf(Opens below)) Not For Loan 3UPML00031665

Thesis, Undergraduate (BS Applied Mathematics) -- U. P. in Mindanao

This study presents an application of generic algorithm (GA) on university course timetabling problem using penalty functions were utilized as fitness function, penalizing only on the soft constraints since all hard constraints must be satisfied. One is static penalty function having a fixed penalty parameter in the entire optimization process, and dynamic penalty function where the penalty factors are dependent on the current generation. In comparing the solution between the best for static penalty and the best for dynamic penalty, the later has relatively lower constraint violation making that solution better than the static penalty. Thus, dynamic penalty function is a better performer than static penalty function as a fitness function in a GA optimization process, although the soft constraints were partitioned into equality and inequality constraints, it is not clear which among the constraints is more violated because they have a different degree of penalty

There are no comments on this title.

to post a comment.

Click on an image to view it in the image viewer

Local cover image Local cover image
 
University of the Philippines Mindanao
The University Library, UP Mindanao, Mintal, Tugbok District, Davao City, Philippines
Email: library.upmindanao@up.edu.ph
Contact: (082)295-7025
Copyright @ 2022 | All Rights Reserved