research
∙
09/15/2023
Neural Network Driven, Interactive Design for Nonlinear Optical Molecules Based on Group Contribution Method
A Lewis-mode group contribution method (LGC) – multi-stage Bayesian neur...
share
research
∙
08/27/2023
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy
Machine learning has been adapted to help solve NP-hard combinatorial op...
research
∙
07/18/2023
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
Submodular maximization arises in many applications, and has attracted a...
research
∙
06/29/2023
Macro Placement by Wire-Mask-Guided Black-Box Optimization
The development of very large-scale integration (VLSI) technology has po...
research
∙
06/05/2023
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Evolutionary algorithms (EAs) have been widely and successfully applied ...
research
∙
05/10/2023
Fast Teammate Adaptation in the Presence of Sudden Policy Change
In cooperative multi-agent reinforcement learning (MARL), where an agent...
research
∙
05/10/2023
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Cooperative multi-agent reinforcement learning (CMARL) has shown to be p...
research
∙
02/02/2023
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they...
research
∙
12/04/2022
Can Evolutionary Clustering Have Theoretical Guarantees?
Clustering is a fundamental problem in many areas, which aims to partiti...
research
∙
10/13/2022
Multi-agent Dynamic Algorithm Configuration
Automated algorithm configuration relieves users from tedious, trial-and...
research
∙
10/11/2022
Analysis of Expected Hitting Time for Designing Evolutionary Neural Architecture Search Algorithms
Evolutionary computation-based neural architecture search (ENAS) is a po...
research
∙
10/04/2022
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive b...
research
∙
08/09/2022
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Generating agents that can achieve Zero-Shot Coordination (ZSC) with uns...
research
∙
05/03/2022
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
Subset selection, which aims to select a subset from a ground set to max...
research
∙
04/12/2022
Neural Network Pruning by Cooperative Coevolution
Neural network pruning is a popular model compression method which can s...
research
∙
03/22/2022
Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection
Evolutionary algorithms (EAs) have been widely used to solve multi-objec...
research
∙
03/20/2022
An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs
Topology Optimization (TO) provides a systematic approach for obtaining ...
research
∙
11/02/2021
HydraText: Multi-objective Optimization for Adversarial Textual Attack
The field of adversarial textual attack has significantly grown over the...
research
∙
10/18/2021
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Given a ground set of items, the result diversification problem aims to ...
research
∙
04/20/2021
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Evolutionary algorithms (EAs) are general-purpose optimization algorithm...
research
∙
01/29/2021
An adaptive artificial neural network-based generative design method for layout designs
Layout designs are encountered in a variety of fields. For problems with...
research
∙
09/14/2020
Accelerating gradient-based topology optimization design with dual-model neural networks
Topology optimization (TO) is a common technique used in free-form desig...
research
∙
10/12/2019
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions
As evolutionary algorithms (EAs) are general-purpose optimization algori...
research
∙
10/12/2019
Bayesian Optimization using Pseudo-Points
Bayesian optimization (BO) is a popular approach for expensive black-box...
research
∙
07/28/2019
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
In real-world optimization tasks, the objective (i.e., fitness) function...
research
∙
06/17/2019
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful real-world appl...
research
∙
10/16/2018
Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization
In this paper, we study the problem of monotone (weakly) DR-submodular c...
research
∙
10/11/2018
Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to dea...
research
∙
12/31/2017
ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization
Recent advances of derivative-free optimization allow efficient approxim...
research
∙
11/20/2017
Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purp...
research
∙
11/02/2017
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
In many real-world optimization problems, the objective function evaluat...
research
∙
06/10/2016
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions
Evolutionary algorithms (EAs) are population-based general-purpose optim...
research
∙
11/20/2013
Analyzing Evolutionary Optimization in Noisy Environments
Many optimization tasks have to be handled in noisy environments, where ...
research
∙
11/03/2011