By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Damas, M. ; Dept. de Arquitectura y Tecnologia de Comput., Granada Univ., Spain ; Salmeron, M. ; Diaz, A. ; Ortega, J.
more authors

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

Published in:

Evolutionary Computation, 2000. Proceedings of the 2000 Congress on  (Volume:1 )

Date of Conference:

2000