Parallel Distributed Genetic Programming using Long-term Memory for Dynamic Scheduling Problems
Tomohiro Hayashida, Daisuke Hirotani, Ichiro Nishizaki, Shinya Sekizaki, Ibuki Maeda
Genetic Programming (GP) is an evolutionary computation method that optimizes the rules defining the relationship between environmental states and system output. GP is an effective method for dynamic environments in which the information repeatedly changes multiple times. On the other hand, in GP, the rules are evaluved as the environmental state changes, so the rules acquired in the distant past disappear in time, and re-learning is required in a dynamic environment. This paper proposes an optimization method for the dynamic scheduling problem where new jobs arrive intermittently. Specifically, a method to improve the learning efficiency of GP in such a periodic dynamic environment by dividing the population into several subpopulations and recording the environmental states or their characteristics. This paper conducts some numerical experiments on the dynamic scheduling problems in which new jobs arrive irregularly to verify the usefulness of the proposed method.
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