Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization

TL;DR: MaOALO is a novel MaO algorithm that enhances convergence and diversity using reference point, niche preserve and IFM mechanisms. It outperforms other MaO algorithms on various real-world and standard benchmark problems.

TL;DR: The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances, and suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.

TL;DR: In this article, the authors propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators and can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used.

TL;DR: The idea that people's preferences are often constructed in the process of elicitation is derived from studies demonstrating that normatively equivalent elicitation (e.g., choice and pricing) give rise to systematically different responses.

TL;DR: A steady-state EMOA is proposed that features a selection operator based on the hypervolume measure combined with the concept of non-dominated sorting, thereby focussing on interesting regions of the Pareto front.