The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

In many traditional job scheduling settings, it is assumed that one knows the time it will take
for a job to complete service. In such cases, strategies such as shortest job first can be used …

We study the problem of maximizing a function that is approximately submodular under a
cardinality constraint. Approximate submodularity implicitly appears in a wide range of …

In many machine learning applications, it is important to explain the predictions of a black-
box classifier. For example, why does a deep neural network assign an image to a particular …

Maximizing on k-submodular functions subject to size constraint has received extensive
attention recently. In this paper, we investigate a more realistic scenario of this problem that …

The problem of selecting the best $ k $-element subset from a universe is involved in many
applications. While previous studies assumed a noise-free environment or a noisy …

Subset selection, aiming to select the best subset from a ground set with respect to some
objective function, is a fundamental problem with applications in many areas, such as …

Stochastic optimization of continuous objectives is at the heart of modern machine learning.
However, many important problems are of discrete nature and often involve submodular …

We address the problem of maximizing an unknown submodular function that can only be
accessed via noisy evaluations. Our work is motivated by the task of summarizing content …