Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Evolutionary algorithms for deterministic and stochastic unconstrained function optimization
Download
index.pdf
Date
2004
Author
Koçkesen, Talip Kerem
Metadata
Show full item record
Item Usage Stats
242
views
90
downloads
Cite This
Most classical unconstrained optimization methods require derivative information. Different methods have been proposed for problems where derivative information cannot be used. One class of these methods is heuristics including Evolutionary Algorithms (EAs). In this study, we propose EAs for unconstrained optimization under both deterministic and stochastic environments. We design a crossover operator that tries to lead the algorithm towards the global optimum even when the starting solutions are far from the optimal solution. We also adapt this algorithm to a stochastic environment where there exist only estimates for the function values. We design new parent selection schemes based on statistical grouping methods and a replacement scheme considering existing statistical information. We test the performance of our algorithms using functions from the literature and newly introduced functions and obtain promising results.
Subject Keywords
Production management.
URI
http://etd.lib.metu.edu.tr/upload/12605583/index.pdf
https://hdl.handle.net/11511/15011
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Neural network calibrated stochastic processes: forecasting financial assets
Giebel, Stefan; Rainer, Martin (Springer Science and Business Media LLC, 2013-03-01)
If a given dynamical process contains an inherently unpredictable component, it may be modeled as a stochastic process. Typical examples from financial markets are the dynamics of prices (e.g. prices of stocks or commodities) or fundamental rates (exchange rates etc.). The unknown future value of the corresponding stochastic process is usually estimated as the expected value under a suitable measure, which may be determined from distribution of past (historical) values. The predictive power of this estimati...
Effective optimization with weighted automata on decomposable trees
Ravve, E. V.; Volkovich, Z.; Weber, Gerhard Wilhelm (Informa UK Limited, 2014-01-02)
In this paper, we consider quantitative optimization problems on decomposable discrete systems. We restrict ourselves to labeled trees as the description of the systems and we use weighted automata on them as our computational model. We introduce a new kind of labeled decomposable trees, sum-like weighted labeled trees, and propose a method, which allows us to reduce the solution of an optimization problem, defined in a fragment of Weighted Monadic Second Order Logic, on such a tree to the solution of effec...
Multilevel graph partitioning: an evolutionary approach
Kucupetek, S; Polat, Faruk; Oguztuzun, H (Informa UK Limited, 2005-05-01)
The graph partitioning problem is defined as that of dividing the vertices of an undirected graph into a set of balanced parts through the removal of a set of edges, whose size is to be minimized. A number of researchers have investigated multilevel schemes, which coarsen the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partitioning of the original graph. In this paper, a genetic algorithm for the coarsening phase of a multilevel scheme for graph ...
Assessment of criteria - rich rankings for environmental policy making
Yeralan, Sencer; Ozdoglar, Mehmet Rasit; Azizoğlu, Meral (Inderscience Publishers, 2011-12-01)
This paper illustrates the use of mathematical programming techniques to extract more information out of composite indexes (e.g., the EPI-2008) that would assist decision makers. While recognising the qualitative aspects of such decision making, in order to support and guide the policy making process, we develop analytical tools to assist the process. We carefully delineate our models to be limited only to the provable quantitative properties of the available objective data. However, such data are processed...
Batch scheduling duling of incompatible jobs on a single reactor with dynamic arrivals
Korkmaz, Gediz; Kayalıgil, Sinan; Department of Industrial Engineering (2004)
In this study, a single machine batch-scheduling problem with incompatible jobs and dynamic arrivals is examined. The objective function is the minimization of the total flow time of the jobs. For solving problems a case specific branch and bound algorithm with a heuristic upper bound scheme and two alternative lower bound procedures is used. An extensive computational experiment is conducted to investigate the effects of certain parameters on the computation time. For the most difficult parameter combinati...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
T. K. Koçkesen, “Evolutionary algorithms for deterministic and stochastic unconstrained function optimization,” M.S. - Master of Science, Middle East Technical University, 2004.