Search Results for author:
Found 25 papers, 4 papers with code
An Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization
Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence.
HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design
This paper introduces a paradigm shift by considering multiple attributes in AMP design.
A Composite Decomposition Method for Large-Scale Global Optimization
Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method.
Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms.
Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems.
Efficient Evaluation Methods for Neural Architecture Search: A Survey
In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs.
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.
Learn to Adapt for Monocular Depth Estimation
To boost the transferability of depth estimation models, we propose an adversarial depth estimation task and train the model in the pipeline of meta-learning.
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
The paper conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform.
Snippet Policy Network for Multi-class Varied-length ECG Early Classification
To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data.
An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA.
System Design and Analysis for Energy-Efficient Passive UAV Radar Imaging System using Illuminators of Opportunity
Then, a set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and communication.
A Survey on Evolutionary Neural Architecture Search
Deep Neural Networks (DNNs) have achieved great success in many applications.
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency
This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures
Data mining on existing CNN can discover useful patterns and fundamental sub-comments from their architectures, providing researchers with strong prior knowledge to design proper CNN architectures when they have no expertise in CNNs.
Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy
Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities.
Automatically Evolving CNN Architectures Based on Blocks
The proposed algorithm is evaluated on CIFAR10 and CIFAR100 against 18 state-of-the-art peer competitors.
Automatically designing CNN architectures using genetic algorithm for image classification
Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years.
Improved Regularity Model-based EDA for Many-objective Optimization
Finally, by assigning the Pareto-optimal solutions to the uniformly distributed reference vectors, a set of solutions with excellent diversity and convergence is obtained.
IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems
Inverted Generational Distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multi- and many-objective evolutionary algorithms.
Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations
Specifically, error classification rate on MNIST with $1. 15\%$ is reached by the proposed algorithm consistently, which is a very promising result against state-of-the-art unsupervised DL algorithms.
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification
Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years.
Evolving Deep Convolutional Neural Networks for Image Classification
Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights.
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications.
