A survey on recent progress in the theory of evolutionary algorithms for discrete optimization
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 …
progress since the early 2010s. This survey summarizes some of the most important recent …
Efficient combinatorial optimization for word-level adversarial textual attack
Over the past few years, various word-level textual attack approaches have been proposed
to reveal the vulnerability of deep neural networks used in natural language processing …
to reveal the vulnerability of deep neural networks used in natural language processing …
Large-scale quantum approximate optimization via divide-and-conquer
Quantum approximate optimization algorithm (QAOA) is a promising hybrid quantum-
classical algorithm for solving combinatorial optimization problems. However, it cannot …
classical algorithm for solving combinatorial optimization problems. However, it cannot …
Optimization of chance-constrained submodular functions
Submodular optimization plays a key role in many real-world problems. In many real-world
scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that …
scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that …
[HTML][HTML] Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization
algorithm, and have shown empirically good performance in solving various real-word …
algorithm, and have shown empirically good performance in solving various real-word …
Effective and imperceptible adversarial textual attack via multi-objectivization
The field of adversarial textual attack has significantly grown over the past few years, where
the commonly considered objective is to craft adversarial examples (AEs) that can …
the commonly considered objective is to craft adversarial examples (AEs) that can …
Capacity constrained influence maximization in social networks
Influence maximization (IM) aims to identify a small number of influential individuals to
maximize the information spread and finds applications in various fields. It was first …
maximize the information spread and finds applications in various fields. It was first …
Multiobjective evolutionary algorithms are still good: Maximizing monotone approximately submodular minus modular functions
C Qian - Evolutionary Computation, 2021 - direct.mit.edu
As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent
theoretical studies have tried to analyze their performance for solving general problem …
theoretical studies have tried to analyze their performance for solving general problem …
Robust subset selection by greedy and evolutionary Pareto optimization
C Bian, Y Zhou, C Qian - arXiv preprint arXiv:2205.01415, 2022 - arxiv.org
Subset selection, which aims to select a subset from a ground set to maximize some
objective function, arises in various applications such as influence maximization and sensor …
objective function, arises in various applications such as influence maximization and sensor …
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Chance constraints are frequently used to limit the probability of constraint violations in real-
world optimization problems where the constraints involve stochastic components. We study …
world optimization problems where the constraints involve stochastic components. We study …