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This paper aims to be a short literature review, presenting the top five most promising algorithms for scheduling, as identified by us from the technical and scientific literature of the past years: Task Swap, Squeaky Wheel Optimization, Value-Biased Stochastic Search, Bee Colony Optimization And Temporal Difference (lambda), from reinforcement learning. We wanted to cover permutation-state methods,...
This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the...
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