Resource constrained job scheduling is a hard combinatorial optimisation
problem that originates in the mining industry. Off-the-shelf solvers cannot
solve this problem satisfactorily in reasonable timeframes, while other
solution methods such as many evolutionary computation methods and
matheuristics cannot guarantee optimality and require low-level customisation
and specialised heuristics to be effective. This paper addresses this gap by
proposing a genetic programming algorithm to discover efficient search
strategies of constraint programming for resource-constrained job scheduling.
In the proposed algorithm, evolved programs represent variable selectors to be
used in the search process of constraint programming, and their fitness is
determined by the quality of solutions obtained for training instances. The
novelties of this algorithm are (1) a new representation of variable selectors,
(2) a new fitness evaluation scheme, and (3) a pre-selection mechanism. Tests
with a large set of random and benchmark instances, the evolved variable
selectors can significantly improve the efficiency of constraining programming.
Compared to highly customised metaheuristics and hybrid algorithms, evolved
variable selectors can help constraint programming identify quality solutions
faster and proving optimality is possible if sufficiently large run-times are
allowed. The evolved variable selectors are especially helpful when solving
instances with large numbers of machines.

提出了一种遗传规划算法，用于发现资源约束作业调度的约束规划的高效搜索策略，通过进化程序表示变量选择器，并通过质量评估方案确定其适应度，结果显示进化的变量选择器能够显著提高约束规划的效率。

基于遗传约束编程的资源受限作业调度

Genetic-based Constraint Programming for Resource Constrained Job  Scheduling

We develop a flexible Gaussian Process (GP) framework for learning the
covariance structure of Age- and Year-specific mortality surfaces. Utilizing
the additive and multiplicative structure of GP kernels, we design a genetic
programming algorithm to search for the most expressive kernel for a given
population. Our compositional search builds off the Age-Period-Cohort (APC)
paradigm to construct a covariance prior best matching the spatio-temporal
dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA)
on synthetic case studies to validate the ability of the GA to recover APC
structure, and on real-life national-level datasets from the Human Mortality
Database. Our machine-learning based analysis provides novel insight into the
presence/absence of Cohort effects in different populations, and into the
relative smoothness of mortality surfaces along the Age and Year dimensions.
Our modelling work is done with the PyTorch libraries in Python and provides an
in-depth investigation of employing GA to aid in compositional kernel search
for GP surrogates.

本文利用柔性高斯过程框架，设计了遗传编程算法来搜索适合于特定人口的最具表现性的内核。研究旨在揭示不同人口中队列效应有无及死亡率表面在年龄和年份维度上的相对平滑性。用编程算法对全国范围的真实数据集进行验证。

高斯过程内核表达的死亡率模型

Expressive Mortality Models through Gaussian Process Kernels

Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.

本文提出一种新颖的博弈论机器学习方法，该方法自然地合并机器学习和博弈论，通过双层优化框架学习拍卖机制以实现经验收入最大化。实验证明，该方法能够比几个基线方法产生更有效的拍卖机制。