Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Virtual loser genetic algorithm for dynamic environments
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Enhancing the virtual loser genetic algorithm for dynamic environments
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Extended virtual loser genetic algorithm for the dynamic traveling salesman problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper describes a memory enhanced evolutionary algorithm (EA) approach to the dynamic job shop scheduling problem. Memory enhanced EAs have been widely investigated for other dynamic optimization problems with changing fitness landscapes, but only when associated with a fixed search space. In dynamic scheduling, the search space shifts as jobs are completed and new jobs arrive, so memory entries that describe specific points in the search space will become infeasible over time. The relative importances of jobs in the schedule also change over time, so previously good points become increasingly irrelevant. We describe a classifier-based memory for abstracting and storing information about schedules that can be used to build similar schedules at future times. We compared the memory enhanced EA with a standard EA and several common EA diversity techniques both with and without memory. The memory enhanced EA improved performance over the standard EA, while diversity techniques decreased performance.