Evolutionary deep learning: A survey

Zhan, Zhi-Hui; Li, Jianyu; Zhang, Jun

doi:10.1016/j.neucom.2022.01.099

Cited by 107 publications

(31 citation statements)

References 131 publications

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“…Unfortunately, for recognizing plant leaf diseases, these diverse and large datasets have not been gathered. Now, transfer learning (TL) is the efficient method for training the strength of CNN classifier for recognizing plant leaf diseases [33][34][35]. e TL method allows the adaptation of pretrained CNN by retraining them with small dataset and its dispersal is distinct from the larger datasets formerly utilized for training the network from scratch.…”

Section: Resultsmentioning

confidence: 99%

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Tamilvizhi¹,

Rajendran

Anbazhagan

et al. 2022

Mathematical Problems in Engineering

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Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

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Section: Resultsmentioning

confidence: 99%

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Tamilvizhi¹,

Rajendran

Anbazhagan

et al. 2022

Mathematical Problems in Engineering

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“…Specifically, fairness metrics are regarded as extra objectives in the optimization to ensure the fairness of the learning model [22], [29], [30]. Different from conventional learning methods to solve one of the objectives or a scalar function of multiple objectives, the Paretobased multi-objective machine learning [195], [199], [200] simultaneously optimizing multiple objectives has received increased interest recently. The reason is that we can gain a deeper insight into ML problems by analysing the PS and PF.…”

Section: Differences Between Fairness In Optimization and Machine Lea...mentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

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“…volutionary algorithms (EAs) are a kind of meta-heuristic algorithms that are inspired by organism reproduction and natural selection [1], [2]. EAs have achieved significant success in dealing with a variety of real-world problems over the last several decades [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Jiang

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

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Evolutionary deep learning: A survey

Cited by 107 publications

References 131 publications

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Towards Fairness-Aware Multi-Objective Optimization

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

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