Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Engineering industry controllers using neuroevolution
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A reinforcement learning model for supply chain ordering management: An application to the beer game
Decision Support Systems
A Cultural Algorithm for POMDPs from Stochastic Inventory Control
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system
Expert Systems with Applications: An International Journal
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve large instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose a neuroevolutionary approach: using an artificial neural network to approximate the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find good plans.