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Fighting games represent a challenging problem for computer-controlled characters. Therefore, they have attracted considerable research interest. This paper investigates novel multi-objective neuroevolutionary approaches for fighting games focusing on the Fighting Game AI Competition. Considering several objectives shall improve the AI's generalization capabilities when confronted with new opponents. To this end, novel combinations of neuroevolution and multi-objective evolutionary algorithms are explored. Since the variants proposed employ the well-known R2 indicator, we derived a novel faster algorithm for determining the exact R2 contribution. An experimental comparison of the novel variants to existing multi-objective neuroevolutionary algorithms demonstrates clear performance benefits on the test case considered. The best performing algorithm is then used to evolve controllers for the fighting game. Comparing the results with state-of-the-art AI opponents shows very promising results; the novel bot is able to outperform several competitors.

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