Genetic algorithms and neuro-dynamic programming: application to water supply networks

Genetic algorithms, time series prediction, and Monte Carlo simulation are applied to dynamic programming in order to solve complex planning and control problems in which decisions are made in stages, and the states and control belong to a continuous space. Each decision has an immediate associated cost and also affects the cost of future stages. Therefore, a balance is required between a low cost solution at the present and the possible high costs in the future. A hybrid genetic algorithm is used to determine the feasible functioning states in each stage. A procedure for series prediction based on RBF networks allows the uncertainty about state transitions to be avoided and Monte Carlo simulations are used to approximate the cost-to-go function, thus reducing the computational cost of the dynamic programming procedure. As an example, the proposed procedure is applied to a water supply network scheduling problem.