Runtime Analysis of the (μ + 1) GA: Provable Speed-Ups from Strong Drift towards Diverse Populations

Most evolutionary algorithms used in practice heavily employ crossover. In contrast, the rigorous understanding of how crossover is beneficial is largely lagging behind. In this work, we make a considerable step forward by analyzing the population dynamics of the (μ + 1) genetic algorithm when optimizing the Jump benchmark. We observe (and prove via mathematical means) that once the population contains two different individuals on the local optimum, the diversity in the population increases in expectation. From this drift towards more diverse states, we show that a diversity suitable for crossover to be effective is reached quickly and, more importantly, then persists for a time that is at least exponential in the population size μ. This drastically improves over the previously best known guarantee, which is only quadratic in μ. Our new understanding of the population dynamics easily gives stronger performance guarantees. In particular, we derive that population sizes logarithmic in the problem size n already suffice to gain an Ω(n)-factor runtime improvement from crossover (previous works achieved comparable bounds only with μ = Θ(n) or via a non-standard mutation rate). This paper for the hot-off-the-press track at GECCO 2024 summarizes the work Benjamin Doerr, Aymen Echarghaoui, Mohammed Jamal, and Martin S. Krejca: Runtime analysis of the (μ + 1) GA: Provable speed-ups from strong drift towards diverse populations. Conference on Artificial Intelligence, AAAI 2024. AAAI Press, 20683--20691. DOI: 10.1609/aaai.v38i18.30055 [4].