High school weekly timetabling by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimizing Employee Schedules by a Hybrid Genetic Algorithm
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Belief Revision by Lamarckian Evolution
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Automated Solution of a Highly Constrained School Timetabling Problem - Preliminary Results
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Specialised Recombinative Operators for Timetabling Problems
Selected Papers from AISB Workshop on Evolutionary Computing
An Evolutionary Algorithm for Solving the School Time-Tabling Problem
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Journal of Experimental Algorithmics (JEA)
Applying evolutionary computation to the school timetabling problem: The Greek case
Computers and Operations Research
Constraint-Based School Timetabling Using Hybrid Genetic Algorithms
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
An application of genetic algorithms to the school timetabling problem
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
A case study for timetabling in a dutch secondary school
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
An informed genetic algorithm for the high school timetabling problem
SAICSIT '10 Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
Automatic test data generation by multi-objective optimisation
SAFECOMP'06 Proceedings of the 25th international conference on Computer Safety, Reliability, and Security
Automated generation and evaluation of dataflow-based test data for object-oriented software
QoSA'05 Proceedings of the First international conference on Quality of Software Architectures and Software Quality, and Proceedings of the Second International conference on Software Quality
The Interleaved Constructive Memetic Algorithm and its application to timetabling
Computers and Operations Research
A memetic algorithm for the capacitated m-ring-star problem
Applied Intelligence
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Hybrid Genetic Algorithms apply so called hybrid or repair operators or include problem specific knowledge about the problem domain in their mutation and crossover operators. These operators use local search to repair or avoid illegal or unsuitable assignments or just to improve the quality of the solutions already found.Those Hybrid Genetic Algorithms have been successfully applied to different constraint satisfaction and timetabling problems such as the travelling salesman problem, scheduling problems, employee timetabling or high school timetabling.In this paper we describe a Genetic Algorithm for solving the German school timetabling problem. The Genetic Algorithm uses direct representation of the problem and applies an adapted mutation operator as well as several specific repair operators. We redecode the computed improvements to the genotype which establishes a kind of Lamarckian evolution.One of the problems utilising these hybrid operators is how and when to apply them, i.e. how to set the parameters right to achieve the best results. Different approaches have been started to adjust these parameters in an optimal way, but in most cases these adjustments require additional computing time and consequently are quite costly. We tackled this problem by an adaptation mechanism for the repair operators which can be applied without additional computing time. These operators are switched on when the normal Genetic Algorithm does not yield any more improvements. When the Genetic Algorithm then converges again, a reconfiguration step for the operator parameters guides the search out of the local optimum.