Complex engineering design problems, such as those involved in aerospace,
civil, or energy engineering, require the use of numerically costly simulation
codes in order to predict the behavior and performance of the system to be
designed. To perform the design of the systems, these codes are often embedded
into an optimization process to provide the best design while satisfying the
design constraints. Recently, new approaches, called Quality-Diversity, have
been proposed in order to enhance the exploration of the design space and to
provide a set of optimal diversified solutions with respect to some feature
functions. These functions are interesting to assess trade-offs. Furthermore,
complex engineering design problems often involve mixed continuous, discrete,
and categorical design variables allowing to take into account technological
choices in the optimization problem. In this paper, a new Quality-Diversity
methodology based on mixed continuous, discrete and categorical Bayesian
optimization strategy is proposed. This approach allows to reduce the
computational cost with respect to classical Quality - Diversity approaches
while dealing with discrete choices and constraints. The performance of the
proposed method is assessed on a benchmark of analytical problems as well as on
an industrial design optimization problem dealing with aerospace systems.

基于混合连续、离散和类别贝叶斯优化策略，提出了一种新的质量多样性方法论，用于处理复杂工程设计问题，并减少计算成本。

面向混合连续、离散和分类变量的约束优化问题的贝叶斯质量多样性方法

Bayesian Quality-Diversity approaches for constrained optimization  problems with mixed continuous, discrete and categorical variables

The discovery of neural architectures from scratch is the long-standing goal
of Neural Architecture Search (NAS). Searching over a wide spectrum of neural
architectures can facilitate the discovery of previously unconsidered but
well-performing architectures. In this work, we take a large step towards
discovering neural architectures from scratch by expressing architectures
algebraically. This algebraic view leads to a more general method for designing
search spaces, which allows us to compactly represent search spaces that are
100s of orders of magnitude larger than common spaces from the literature.
Further, we propose a Bayesian Optimization strategy to efficiently search over
such huge spaces, and demonstrate empirically that both our search space design
and our search strategy can be superior to existing baselines. We open source
our algebraic NAS approach and provide APIs for PyTorch and TensorFlow.

通过代数视角来表达网络结构，我们提出了一种更具有广泛性的方法来设计搜索空间，并提出了一种 Bayesian Optimization 策略来高效地在巨大的搜索空间中搜索，实验证明我们的搜索空间设计和搜索策略可以优于现有基准。