Self-Learning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F13%3AA14015ZZ" target="_blank" >RIV/61988987:17610/13:A14015ZZ - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Self-Learning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints
Original language description
Timetabling Problem is NP-hard combinatorial optimization problem which lacks analytical solution methods. Here, for solving this problem we present a specific genetic algorithm with fuzzy constraints. Our method incorporates a self-learning genetic algorithm using indirect representation based on event priorities, and heuristic local search operators to tackle real world timetabling problem. By fuzzy sets we measure violation of soft constraints in fitness function to take care of inherent uncertaintyand vagueness involved in real life data. The proposed technique satisfies all hard constraints of problem and achieves significantly better score in satisfying soft constraints. The algorithm is computationally effective as it is demonstrated on a smallrealistic example for which an optimal solution is known by exhaustive calculation. The structure of the algorithm enables parallel computations being necessary for solving large real life problems.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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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)

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

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