On the integrity of performance comparison for evolutionary multi-objective optimisation algorithms
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Journal: Advances in Intelligent Systems and Computing
© Springer Nature Switzerland AG 2019. This paper proposes the notion that the experimental results and performance analyses of newly developed algorithms in the field of multi-objective optimisation may not offer sufficient integrity for hypothesis testing. The reason for this is that many implementations exist of the same optimisation algorithms, and these may vary in behaviour due to the interpretation of the developer. This is demonstrated through the comparison of three implementations of the popular Non-dominated Sorting Genetic Algorithm II (NSGA-II) from well-regarded frameworks using the hypervolume indicator. The results show that of the thirty considered comparison cases, only four indicate that there was no significant difference between the performance of either implementation.