Nature has learned from itself from the very beginning of Earth, with manifold processes and intelligent behaviors that have naturally evolved over ages to attain high levels of adaptability and efficiency. It is now time for researchers, lecturers, and practitioners interested in Nature-Inspired optimization to shift their target and span the application of this algorithmic branch to these optimization problems, far less studied so far by the community than other formulated optimization problems. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.
Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Javier Del Ser Lorente. We are IntechOpen, the world's leading publisher of Open Access books.
- Helping English Language Learners Succeed.
- Metallic Chains / Chains of Metals (Handbook of Metal Physics)?
- Handbook of Textile Design: Principles, Processes, and Practice.
- Mobile Guide to BlackBerry!
- Understanding Biostatistics!
- The Continental Drift Controversy. Evolution into Plate Tectonics?
- MFA vs NYC: The Two Cultures of American Fiction.
Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Introduction Optimization is one of the most studied fields in the wide field of artificial intelligence. Dynamic optimization In optimization problems, it is often the case that the parameters based on which fitness function s and constraints are defined remain unaltered over the period of time in which the solution obtained by the solver is considered to be optimal.
Stochastic optimization Stochastic optimization is another problem variant that finds its motivation in real application scenarios.
An overview of gradient descent optimization algorithms
Robust optimization The third class of optimization problems targeted by this chapter is robust optimization, which denotes a branch of problems where one or more variables that compose the problem is also subject to uncertainty. Conclusions This introductory chapter highlights the potential that Nature-Inspired solvers may bring to stochastic, robust, and dynamic optimization problems.
More Print chapter. How to cite and reference Link to this chapter Copy to clipboard. Available from:. Over 21, IntechOpen readers like this topic Help us write another book on this subject and reach those readers Suggest a book topic Books open for submissions. More statistics for editors and authors Login to your personal dashboard for more detailed statistics on your publications.
CE 599 - Uncertainty Modeling and Stochastic Optimization - Fall 2008
Access personal reporting. A popular way of performing the above task, often dubbed quantification, is to use supervised learning in order to train a prevalence estimator from labeled data.
- Organic Syntheses An annual publication of satisfactory methods.
- Table of contents.
- Fast Food Maniac: From Arbys to White Castle, One Mans Supersized Obsession with Americas Favorite Food.
- Footer menu.
In the literature there are several performance metrics for measuring the success of such prevalence estimators. In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures.
Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure.
Stochastic Optimization Techniques for Quantification Performance Measures
Email or Customer ID. Forgot password? Old Password. New Password.
The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems
Password Changed Successfully Your password has been changed. Returning user. Request Username Can't sign in?