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Found 45 papers, 17 papers with code
Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables
In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold.
Self-Improved Learning for Scalable Neural Combinatorial Optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint.
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences.
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker.
Multi-Objective Reinforcement Learning
Multi-Task Learning
+1
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Our work marks a step forward in effectively and efficiently aligning models to diverse and intricate human preferences in a controllable and Pareto-optimal manner.
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security.
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks
Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
Algorithm Evolution Using Large Language Model
In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL).
Evolutionary Pareto Set Learning with Structure Constraints
In our approach, the Pareto optimality can be traded off with a preferred structure among the whole solution set, which could be crucial for many real-world problems.
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
Continuation Path Learning for Homotopy Optimization
Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems.
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Vehicle routing is a well-known optimization research topic with significant practical importance.
Pareto Set Learning for Expensive Multi-Objective Optimization
This work represents the first attempt to model the Pareto set for expensive multi-objective optimization.
Pareto Set Learning for Neural Multi-objective Combinatorial Optimization
In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure.
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images
By representing object instances within the same category as shape and appearance variation of a shared NeRF template, our proposed method can achieve dense shape correspondences reasoning on images for a wide range of object classes.
Preference Conditioned Neural Multi-objective Combinatorial Optimization
In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure.
Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild
Specifically, on the basis of the intrinsic imaging degradation model of RGB images from HS images, we progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective unsupervised camera spectral response function estimation.
Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images
Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient.
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
Self-supervised Symmetric Nonnegative Matrix Factorization
Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information.
Clustering Ensemble Meets Low-rank Tensor Approximation
The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e. g., spectral clustering.
Maximum Entropy Subspace Clustering Network
Furthermore, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module.
Controllable Pareto Multi-Task Learning
With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together.
Graph Neural Network Encoding for Community Detection in Attribute Networks
Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables.
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively.
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms.
Pareto Multi-Task Learning
Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems
For a sum-of-the-parts combinatorial optimization problem, we propose to decompose its original objective into two sub-objectives with controllable correlation.
Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem
We first define the Homotopic Convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP.
Nonlinear Collaborative Scheme for Deep Neural Networks
Different from them, in this paper, we aim to link the generalization ability of a deep network to optimizing a new objective function.
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
The proposed algorithm uses the Bayesian neural network as the scalable surrogate model.
Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search
In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed.
A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation
In this paper, we propose an efficient approximated rank one update for covariance matrix adaptation evolution strategy (CMA-ES).
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure
EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution.
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
It decomposes a multi-objective optimization problem into several single-objective optimization subproblems, each of which is usually defined as a scalarizing function using a weight vector.
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
In particular, the stable matching between subproblems and solutions, which achieves an equilibrium between their mutual preferences, implicitly strikes a balance between the convergence and diversity.
Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm
Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run.
