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Home List of Titles A comparison between genetic algorithms and evolutionary programming based on cutting stock problem
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/37964
- A comparison between genetic algorithms and evolutionary programming based on cutting stock problem
- Chiong, Raymond; Ooi, K. B.
- Genetic Algorithms (GA) and Evolutionary Programming (EP) are two well-known optimization methods that belong to the class of Evolutionary Algorithms (EA). Both methods have generally been recognized to have successfully solved many problems in recent years, especially with respect to engineering and industrial problems. Even though they are two different types of EA, the two methods share a lot of commonalities in the genetic operators they use and the way they mimic natural evolution. This paper aims to bring forth an introductory review on how these two methods tackle the one-dimensional Cutting Stock Problem (CSP). We draw comparison on the effectiveness of GA and EP in solving CSP, and propose an improved algorithm using a combination of the two methods based on our observations. In the concluding remarks, some future works are suggested for further investigations.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Sarawak Campus. School of Information Technology and Multimedia
- Lecture notes in engineering and computer science: Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2006), Kowloon, Hong Kong, 20-22 June 2006, pp. 645-650
- Publication year
- Cutting stock problem; Evolutionary programming; Genetic algorithms; Optimisation methods
- Newswood Limited
- Publisher URL
- Peer reviewed