Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Schedule Flow Shop Releases
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Optimization Using A Penalty Function
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Use of Rules and Preferences for Schedule Builders in Genetic Algorithms for Production Scheduling
Selected Papers from AISB Workshop on Evolutionary Computing
An Ambulance Crew Rostering System
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Learning General Solutions through Multiple Evaluations during Development
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Examination timetabling using late acceptance hyper-heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Choosing the fittest subset of low level heuristics in a hyperheuristic framework
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Using hyperheuristics under a GP framework for financial forecasting
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
On the investigation of hyper-heuristics on a financial forecasting problem
Annals of Mathematics and Artificial Intelligence
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This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.