Very recently, the first mathematical runtime analyses for the NSGA-II, the
most common multi-objective evolutionary algorithm, have been conducted.
Continuing this research direction, we prove that the NSGA-II optimizes the
OneJumpZeroJump benchmark asymptotically faster when crossover is employed.
Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt,
this is the first time such an advantage of crossover is proven for the
NSGA-II. Our arguments can be transferred to single-objective optimization.
They then prove that crossover can speed up the $(\mu+1)$ genetic algorithm in
a different way and more pronounced than known before. Our experiments confirm
the added value of crossover and show that the observed advantages are even
larger than what our proofs can guarantee.

本文通过数学分析证实采用交叉操作时，NSGA-II 可以更快地优化 OneJumpZeroJump benchmark，并证明了这种交叉操作对单目标优化也有帮助，实验结果与证明相符。

NSGA-II 的运行时分析：从交叉算子中可证明的加速

Runtime Analysis for the NSGA-II: Provable Speed-Ups From Crossover

Latest algorithms for automatic neural architecture search perform remarkable
but are basically directionless in search space and computational expensive in
training of every intermediate architecture. In this paper, we propose a method
for efficient architecture search called EENA (Efficient Evolution of Neural
Architecture). Due to the elaborately designed mutation and crossover
operations, the evolution process can be guided by the information have already
been learned. Therefore, less computational effort will be required while the
searching and training time can be reduced significantly. On CIFAR-10
classification, EENA using minimal computational resources (0.65 GPU-days) can
design highly effective neural architecture which achieves 2.56% test error
with 8.47M parameters. Furthermore, the best architecture discovered is also
transferable for CIFAR-100.

本文介绍了一种称为 EENA 的有效神经体系结构搜索方法，该方法通过精心设计的变异和交叉操作进行演化，可以在较少的计算资源下，显著降低搜索和训练时间，并设计出在 CIFAR-10 数据集上效果显著的神经体系结构。

高效的神经结构进化算法（EENA）

EENA: Efficient Evolution of Neural Architecture

Multidimensional genetic programming represents candidate solutions as sets
of programs, and thereby provides an interesting framework for exploiting
building block identification. Towards this goal, we investigate the use of
machine learning as a way to bias which components of programs are promoted,
and propose two semantic operators to choose where useful building blocks are
placed during crossover. A forward stagewise crossover operator we propose
leads to significant improvements on a set of regression problems, and produces
state-of-the-art results in a large benchmark study. We discuss this
architecture and others in terms of their propensity for allowing heuristic
search to utilize information during the evolutionary process. Finally, we look
at the collinearity and complexity of the data representations that result from
these architectures, with a view towards disentangling factors of variation in
application.

本研究探讨了基于多维基因编程的演化计算方法，使用机器学习技术选择性推进程序构建模块，提出了前向逐步杂交操作符，并在回归问题研究中取得了显著的改进和最先进的结果。最后，本文研究了该架构和其他架构在使用启发式搜索期间利用信息的潜力，并从应用程序中解释了数据表示的共线性和复杂性。