DESIGN AND IMPLEMENTATION OF GENETIC ALGORITHMS FOR TIMETABLE GENERATION

DESIGN AND IMPLEMENTATION OF GENETIC ALGORITHMS FOR TIMETABLE GENERATION

Abstract:
Timetable generation is a challenging task in various domains, such as educational institutions, transportation systems, and workforce scheduling. In recent years, genetic algorithms (GAs) have emerged as effective optimization techniques for solving complex scheduling problems. This abstract presents a study on the design and implementation of genetic algorithms specifically tailored for timetable generation.

The objective of this research is to develop a robust and efficient GA-based approach that can generate optimal or near-optimal timetables while considering multiple constraints and preferences. The proposed solution employs a genetic representation for timetables and utilizes genetic operators such as selection, crossover, and mutation to evolve a population of potential solutions over successive generations.

The design of the genetic algorithm involves defining appropriate chromosome representations, fitness evaluation functions, and genetic operators that are customized to suit the specific requirements of timetable generation. Various strategies for encoding timetables, handling conflicts, and incorporating domain-specific constraints are explored and evaluated.

The implementation of the genetic algorithm is carried out using a programming language and framework suitable for optimization tasks. The study investigates the impact of different parameter settings, population sizes, and evolutionary operators on the performance and convergence of the algorithm. Furthermore, techniques for parallelization and optimization of the algorithm are explored to enhance its efficiency and scalability.

To validate the effectiveness of the proposed approach, extensive experiments are conducted using real-world datasets or synthetic scenarios representing diverse scheduling problems. The performance of the algorithm is evaluated based on metrics such as timetable quality, computational efficiency, and solution convergence.

The results of the study demonstrate the capability of genetic algorithms to effectively generate high-quality timetables while considering multiple constraints. The proposed approach offers a flexible and customizable solution that can be adapted to various timetable generation scenarios. Moreover, the study provides insights into the strengths and limitations of genetic algorithms for scheduling problems, paving the way for future research and improvements in the field.

Keywords: Timetable generation, Genetic algorithms, Optimization, Scheduling, Constraints, Chromosome representation.

DESIGN AND IMPLEMENTATION OF GENETIC ALGORITHMS FOR TIMETABLE GENERATION. GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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