A population minimisation process for genetic algorithms and its application to controller optimisation

McGookin, E.W. , Murray-Smith, D.J. and Li, Y. (1997) A population minimisation process for genetic algorithms and its application to controller optimisation. In: Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA 97), Glasgow, UK, 2-4 Sep 1997, pp. 79-84. (doi: 10.1049/cp:19971159)

Abstract

This paper suggests a process which helps reduce the execution time for genetic algorithms by removing the redundancy associated with the saturation effect found in later generations. The process considered minimises the population size as similar individuals occur in the fitter members of the population. As the population size reduces the number of crossover operations decreases and the apparent mutation rate increases. This increase in variation allows better avoidance of local optimal solutions. The process is evaluated by considering results obtained from its application to a submarine controller optimisation problem.

• Item Type: Conference Proceedings
• Additional Information: IEE Conference Publication No. 446. Published by IEE, 1997.
• Keywords: Optimisation, genetic algorithm, crossover, mutation, population, control, marine system, submarine
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: McGookin, Dr Euan and Murray-Smith, Professor David and Li, Professor Yun
• Authors: McGookin, E.W., Murray-Smith, D.J., and Li, Y.
• Subjects: Q Science > QA Mathematics > QA76 Computer software; T Technology > TJ Mechanical engineering and machinery; T Technology > TK Electrical engineering. Electronics Nuclear engineering; V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy