Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/39742
Title: Regression assisted evolutionary optimization
Authors: Ayyappan, Murugan
Keywords: DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
Issue Date: 2010
Abstract: Evolutionary optimization is widely used in many applications, like the aerospace industry, manufacturing sector, biomedicine, theoretical physics, and so on. However, the problem that most of these applications have is that the optimization process is computationally expensive. Some of it can be attributed to the nature of evolutionary algorithm as such, but most of it is attributed to cost involved in the evaluation function. As some industrial applications involve complex calculations to evaluate a single solution, optimization takes an intractably long time to complete. The aim of this project is to reduce the number of these evaluations during an optimization run, which would in turn reduce the computational time of the evolutionary optimization. The project involves a regression assisted evolutionary optimization algorithm that attempts to do this. Implementation details and experimental results showing the algorithm’s efficiency are presented.
URI: http://hdl.handle.net/10356/39742
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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