This paper begins with the optimisation of three test functions using a genetic algorithm and describes a statistical analysis on the effects of the choice of crossover technique, parent selection strategy and mutation. The paper then examines the use of a genetic algorithm to optimize the functional form of a polynomial fit to experimental data; the aim being to locate the global optimum of the data. Genetic programming has already been used to locate the functional form of a good fit to sets of data, but genetic programming is more complex than a genetic algorithm. This paper compares the genetic algorithm method with a particular genetic programming approach and shows that equally good results can be achieved using this simpler technique. © 2005 IEEE.
CITATION STYLE
Clegg, J., Dawson, J. F., Porter, S. J., & Barley, M. H. (2005). The use of a genetic algorithm to optimize the functional form of a multi-dimensional polynomial fit to experimental data. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (Vol. 1, pp. 928–934). https://doi.org/10.1109/cec.2005.1554782
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