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.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12605583/index.pdf |
| Date | 01 November 2004 |
| Creators | Kockesen, Kerem Talip |
| Contributors | Ozdemirel, Nur Evin |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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