587 citations · 2k across the 58 of their papers we have counts for
18 papers · 1 filter
EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms
Mai Peng, Delaram Yazdani, Zeneng She +9
Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algo…
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Shangshang Yang, Haiping Ma, Cheng Zhen +4
Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due t…
Evolutionary Reinforcement Learning: A Survey
Hui Bai, Ran Cheng, Yaochu Jin
Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep…
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation
Beichen Huang, Ran Cheng, Zhuozhao Li +2
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-inten…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Nan Li, Lianbo Ma, Guo Yu +3
Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often n…
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
Zhichao Lu, Ran Cheng, Yaochu Jin +2
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the develo…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Jinjin Xu, Yaochu Jin, Wenli Du
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorith…
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
Ye Tian, Xingyi Zhang, Cheng He +2
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators i…
A Federated Data-Driven Evolutionary Algorithm
Jinjin Xu, Yaochu Jin, Wenli Du +1
Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require…
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments
Cuie Yang, Jinliang Ding, Yaochu Jin +1
Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments. This paper proposes a…
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search
Haoyu Zhang, Yaochu Jin, Ran Cheng +1
The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network archit…
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Cheng He, Shihua Huang, Ran Cheng +2
Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is hig…
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Cheng He, Shihua Huang, Ran Cheng +2
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is h…
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Yuan Yuan, Yew-Soon Ong, Liang Feng +7
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 s…
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Joseph Chrol-Cannon, Yaochu Jin, André Grüning
Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process…
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
Ye Tian, Ran Cheng, Xingyi Zhang +1
Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective optimization problems. However, there lacks an up-to-date and…