Real-world problems are often comprised of many objectives and require
solutions that carefully trade-off between them. Current approaches to
many-objective optimization often require challenging assumptions, like
knowledge of the importance/difficulty of objectives in a weighted-sum
single-objective paradigm, or enormous populations to overcome the curse of
dimensionality in multi-objective Pareto optimization. Combining elements from
Many-Objective Evolutionary Algorithms and Quality Diversity algorithms like
MAP-Elites, we propose Many-objective Optimization via Voting for Elites
(MOVE). MOVE maintains a map of elites that perform well on different subsets
of the objective functions. On a 14-objective image-neuroevolution problem, we
demonstrate that MOVE is viable with a population of as few as 50 elites and
outperforms a naive single-objective baseline. We find that the algorithm's
performance relies on solutions jumping across bins (for a parent to produce a
child that is elite for a different subset of objectives). We suggest that this
type of goal-switching is an implicit method to automatic identification of
stepping stones or curriculum learning. We comment on the similarities and
differences between MOVE and MAP-Elites, hoping to provide insight to aid in
the understanding of that approach $\unicode{x2013}$ and suggest future work
that may inform this approach's use for many-objective problems in general.

使用 Many-Objective Evolutionary Algorithms 和 Quality Diversity algorithms 的元素，我们提出了基于投票的多目标精英优化 (MOVE)。MOVE 通过维护适用于目标函数不同子集的精英群体地图来平衡众多目标，并通过在具有 14 个目标的图像神经进化问题上的实验证明 MOVE 是可行的。我们发现该算法的性能取决于解决方案在不同目标子集之间跳跃，并指出这种目标切换是一种隐式的自动识别逐步学习的方法。我们对 MOVE 和 MAP-Elites 之间的相似之处和差异进行了讨论，并提出未来的研究方向以供参考，以便更好地理解这种方法在众多目标问题中的应用。

多目标优化通过精英投票

Many-objective Optimization via Voting for Elites

Quality Diversity (QD) algorithms have been proposed to search for a large
collection of both diverse and high-performing solutions instead of a single
set of local optima. While early QD algorithms view the objective and
descriptor functions as black-box functions, novel tools have been introduced
to use gradient information to accelerate the search and improve overall
performance of those algorithms over continuous input spaces. However a broad
range of applications involve discrete spaces, such as drug discovery or image
generation. Exploring those spaces is challenging as they are combinatorially
large and gradients cannot be used in the same manner as in continuous spaces.
We introduce map-elites with a Gradient-Informed Discrete Emitter (ME-GIDE),
which extends QD optimisation with differentiable functions over discrete
search spaces. ME-GIDE leverages the gradient information of the objective and
descriptor functions with respect to its discrete inputs to propose
gradient-informed updates that guide the search towards a diverse set of high
quality solutions. We evaluate our method on challenging benchmarks including
protein design and discrete latent space illumination and find that our method
outperforms state-of-the-art QD algorithms in all benchmarks.

本文提出了一种基于离散空间和可微函数的拓展质量多样性优化算法（ME-GIDE），利用目标和描述符函数对其离散输入的梯度信息来引导搜索，以获得高质量的多样化解决方案，并在蛋白质设计和离散潜在空间点亮等基准测试中，比现有的质量多样性算法表现更好。

梯度信息驱动的离散空间探索优化算法

Gradient-Informed Quality Diversity for the Illumination of Discrete  Spaces

Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.

使用品质多样性算法对生成对抗网络中的潜空间进行有效探索，进而提取游戏级别，并展示如何在保持风格相似性的情况下提取具有不同级别机制的高质量级别。