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A Comparison of Genetic Algorithms with Various Optimization Methods for Multi-level Problems (1999)
| Venue: | ENG. APPL. OF ARTIFICIAL INTELLIGENCE |
BibTeX
@ARTICLE{El-Beltagy99acomparison,
author = {Mohammed A. El-Beltagy and A. J. Keane},
title = {A Comparison of Genetic Algorithms with Various Optimization Methods for Multi-level Problems},
journal = {ENG. APPL. OF ARTIFICIAL INTELLIGENCE},
year = {1999},
volume = {12},
pages = {639--654}
}
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Abstract
The optimization of noisy or imprecisely specified functions is a common problem occurring in various applications. Models of physical systems can differ according to computational cost, accuracy and precision. In multilevel optimization, where different models of a system are used, there is a great benefit in understanding how many, fast evaluations of limited accuracy may be combined with a few accurate calculations to yield an optimum solution. The combination of different models or levels of representations can lead to an objective function surface characterized by multiple values at a single point, i.e. a function that is subject to noise. This paper compares various optimization methods with genetic algorithms using three different strategies of multilevel optimization. A modified `bump' function is used as an example to compare the different methods and strategies. A sequential mixing strategy applied to a Genetic Algorithm with niche forming is shown to give best results. The paper highlights the need to develop a specialized optimization algorithm for this kind of problem.
Keyphrases
genetic algorithm various optimization method multi-level problem multilevel optimization different model common problem sequential mixing strategy specified function computational cost various application specialized optimization algorithm fast evaluation limited accuracy single point physical system objective function surface different strategy great benefit multiple value niche forming accurate calculation different method optimum solution
