Parameter Sensitivity Patterns in the Plant Propagation Algorithm

Marleen de Jonge, Daan van den Berg


The parameter sensitivity of the plant propagation algorithm’s performance, defined as the maximum impact on attained objective values, is studied on function optimization. The number of offspring and population size are varied across a large range of values and tested on multidimensional benchmark test functions. As the dimensionality of a function increases, the parametric sensitivity shows one of three distinct different patterns: sublinear increase, superlinear increase or an up-down phase transition. We conjecture that the difference in algorithmic behaviour might be due to the intrinsic mathematical properties of the functions themselves.


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