Search Results for author:
Found 47 papers, 11 papers with code
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge.
Escaping Local Optima in Global Placement
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.
Reinforced In-Context Black-Box Optimization
In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search
Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success.
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization
In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception.
Migrant Resettlement by Evolutionary Multi-objective Optimization
In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem.
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II.
Diversity from Human Feedback
DivHF learns a behavior descriptor consistent with human preference by querying human feedback.
Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique.
A Study of Quantisation-aware Training on Time Series Transformer Models for Resource-constrained FPGAs
This study explores the quantisation-aware training (QAT) on time series Transformer models.
Neural Network Driven, Interactive Design for Nonlinear Optical Molecules Based on Group Contribution Method
A Lewis-mode group contribution method (LGC) -- multi-stage Bayesian neural network (msBNN) -- evolutionary algorithm (EA) framework is reported for rational design of D-Pi-A type organic small-molecule nonlinear optical materials is presented.
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 optimization problems.
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations research.
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
This work is an attempt to challenge a common practice in the design of existing MOEAs.
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.
Can Evolutionary Clustering Have Theoretical Guarantees?
We prove that for discrete $k$-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.
Multi-agent Dynamic Algorithm Configuration
MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms
Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks.
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios.
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL).
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets.
Neural Network Pruning by Cooperative Coevolution
Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy.
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-objective optimization problems, and have become the most popular tool.
An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs
Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes.
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model.
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i. e., the greedy algorithm and local search) under both static and dynamic environments.
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
However, due to the inefficient selection mechanisms, these methods cannot fully guarantee both high quality and diversity.
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution.
An adaptive artificial neural network-based generative design method for layout designs
In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Accelerating gradient-based topology optimization design with dual-model neural networks
In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design process of topology optimization.
Self-Guided Evolution Strategies with Historical Estimated Gradients
Evolution Strategies (ES) are a class of black-box optimization algorithms and have been widely applied to solve problems, e. g., in reinforcement learning (RL), where the true gradient is unavailable.
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions
To complement this line of research, this paper studies the problem class of maximizing monotone approximately submodular minus modular functions (i. e., $f=g-c$) with a size constraint, where $g$ is a non-negative monotone approximately submodular function and $c$ is a non-negative modular function, resulting in the objective function $f$ being non-monotone non-submodular.
Bayesian Optimization using Pseudo-Points
Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, robotics.
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.
Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization
Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm.
Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to deal with noise.
ZOOpt: Toolbox for Derivative-Free Optimization
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions.
Subset Selection under Noise
The problem of selecting the best $k$-element subset from a universe is involved in many applications.
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems.
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds.
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions
Our results imply that the increase of population size, while usually desired in practice, bears the risk of increasing the lower bound of the running time and thus should be carefully considered.
Subset Selection by Pareto Optimization
Selecting the optimal subset from a large set of variables is a fundamental problem in various learning tasks such as feature selection, sparse regression, dictionary learning, etc.
Analyzing Evolutionary Optimization in Noisy Environments
On a representative problem where the noise has a strong negative effect, we examine two commonly employed mechanisms in EAs dealing with noise, the re-evaluation and the threshold selection strategies.
