Optimising multi-modal polynomial mutation operators for multi-objective problem classes

This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.