Abstract HypE (Hypervolume Estimation Algorithm) is a fast hypervolume-based evolutionary algorithm designed for many-objective optimization problems.
Unlike traditional hypervolume-based methods that become computationally expensive with increasing objectives, HypE uses Monte Carlo sampling to efficiently estimate hypervolume contributions.
It employs a greedy selection strategy that preferentially retains individuals with higher hypervolume contributions, enabling effective convergence toward the Pareto front.
APIs HypESampler(*, population_size=50, n_samples=4096, mutation=None, mutation_prob=None, crossover=None, crossover_prob=0.9, hypervolume_method="auto", seed=None) population_size: Size of the population for the evolutionary algorithm.