@inproceedings {pub5586,
	title = {Bicriteria optimisation of average and worst-case performance using coevolutionary algorithms},
	author = {Alistair Benford AND Markus Olhofer AND Per  Kristian Lehre AND Tobias Rodemann},
	year = {2024},
	abstract = {A common aim in real-world optimisation problems is to seek a solution which offers the highest performance in the worst-case scenario. Competitive coevolution evolves a population of solutions alongside a population of difficult scenarios in order to find so-called robust solutions. However, solutions with maximal worst-case performance may be excessively conservative and thus exhibit poor performance on more typical scenarios. Existing evolutionary approaches will always favour such solutions over ones which sacrifice only a small amount of average performance for an almost-as-large gain in worst-case performance, despite the latter being favourable in most practical applications. We present a new coevolutionary algorithm which treats average performance and worst-case performance as two objectives of a bicriteria optimisation problem and seeks the corresponding Pareto front. Such an algorithm therefore enables the discovery of solutions with strong performance in both of these metrics, which would otherwise be rejected if optimising for only one. We also provide experimental results on the performance of this algorithm on the design of smart controllers for the management of energy flow between buildings, renewable energy sources, and electric vehicles.},
	publisher = {IEEE},
	booktitle = {IEEE WCCI 2024},
	city = {Yokohama}
}