An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

Abstract

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method – the Sobol’ variance decomposition method – is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol’ test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Keywords:

• multi-objective optimisation
• evolutionary algorithm
• sensitivity
• preferences
• Sobol

Acknowledgements

This research was conducted in Automatic Control and Systems Engineering, University of Sheffield and the first author is grateful for the facilities and support provided by the University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. New parents (of size μ) are selected from a combined set of parents (of size μ) and offspring (of size λ).

Additional information

Funding

The first author would like to thank the National Science Foundation of China [number 61403404]; the National University of Defense Technology [number JC14-05-01] for their financial support.