Regression assisted evolutionary optimization
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 at...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/39742 |
| _version_ | 1824454305772470272 |
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| author | Ayyappan, Murugan |
| author2 | Ong Yew Soon |
| author_facet | Ong Yew Soon Ayyappan, Murugan |
| author_sort | Ayyappan, Murugan |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:20:12Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/39742 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:20:12Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/397422023-03-03T20:44:03Z Regression assisted evolutionary optimization Ayyappan, Murugan Ong Yew Soon School of Computer Engineering Parallel and Distributed Computing Centre DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis 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. Bachelor of Engineering (Computer Engineering) 2010-06-03T08:35:11Z 2010-06-03T08:35:11Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39742 en Nanyang Technological University 46 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis Ayyappan, Murugan Regression assisted evolutionary optimization |
| title | Regression assisted evolutionary optimization |
| title_full | Regression assisted evolutionary optimization |
| title_fullStr | Regression assisted evolutionary optimization |
| title_full_unstemmed | Regression assisted evolutionary optimization |
| title_short | Regression assisted evolutionary optimization |
| title_sort | regression assisted evolutionary optimization |
| topic | DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis |
| url | http://hdl.handle.net/10356/39742 |
| work_keys_str_mv | AT ayyappanmurugan regressionassistedevolutionaryoptimization |