Hot off the Press: Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems is evolutionary diversity optimization (EDO). In this paper, we analyze EDO on a three-objective function LOTZk, which is a modification of the two-objective benchmark function (LeadingOnes, TrailingZeros). We prove that the GSEMO computes a set of all Pareto-optimal solutions in O(kn3) expected iterations. We also analyze the runtime of the GSEMOD algorithm (a modification of the GSEMO for diversity optimization) until it finds a population with the best possible diversity for two different diversity measures: the total imbalance and the sorted imbalances vector. For the first measure we show that the GSEMOD optimizes it in O(kn2 log(n)) expected iterations (which is asymptotically faster than the upper bound on the runtime until it finds a Pareto-optimal population), and for the second measure we show an upper bound of O(k2n3 log(n)) expected iterations. The complementary empirical study shows a very similar behavior for both diversity measures. The results of experiments suggest that our bounds for the total imbalance measure are tight, while the bounds for the imbalances vector are too pessimistic. This paper summarizes the work Denis Antipov, Aneta Neumann, Frank Neumann and Andrew M. Sutton: Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. Evolutionary Computation, 1–23, 2025. [2].