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Surrogate-assisted evolutionary neural architecture search based smart-block discovery

EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Onzo, Bernard-marie Xue, Yu Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Res Grp Guildford GU2 7XH England

Neural architecture search (NAS) has emerged as a powerful method for automating neural network design, yet its high computational cost remains a significant challenge. This paper introduces hybrid training-less neural architecture search (HYTES-NAS), a novel hybrid NAS framework that integrates evolutionary computation with a training-free evaluation strategy, significantly reducing computational demands while maintaining high search efficiency. Unlike conventional NAS methods that rely on full model training, HYTES-NAS leverages a surrogate-assisted scoring mechanism to assess candidate architectures efficiently. Additionally, a smart-block discovery strategy and particle swarm optimisation are employed to refine the search space and accelerate convergence. Experimental results on multiple NAS benchmarks demonstrate that HYTES-NAS achieves superior performance with significantly lower computational cost compared to state-of-the-art NAS methods. This work provides a promising and scalable solution for efficient NAS, making high-performance architecture search more accessible for real-world applications.

关键词： Neural architecture search Training-free evolutionary algorithms Surrogate models

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Critical vector based evolutionary algorithm for large-scale multi-objective optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-27页

作者： Zhu, Shuwei Wang, Wenping Fang, Wei Cui, Meiji Jiangnan Univ Sch Artificial Intelligence & Comp 1800 Lihu Rd Wuxi 214122 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Intelligent Mfg 200 Xiaolingwei St Nanjing 210094 Peoples R China

In this work, we propose a method for solving large-scale multi-objective problems based on problem transformation strategy. The key point of this method lies in how to construct the search subspace. First, the algorithm obtains a set of direction vectors in the decision space, which are combined in pairs to construct a set of subspaces. To obtain direction vectors with a uniform distribution as much as possible, we introduce the opposition-based learning strategy. Then, based on these subspaces, the original high-dimensional problem is transformed into a relatively lower-dimensional problem. A multi-objective evolutionary algorithm is used to quickly obtain a set of quasi-optimal solutions for the transformed lower-dimensional problem, and this set of solutions is further optimized in the original high-dimensional decision space. To validate its performance, the proposed algorithm is compared with six state-of-the-art large-scale multi-objective algorithms on various benchmark test problems, including one practical application. The experimental results demonstrate that the proposed algorithm shows competitive performance for dealing with large-scale multi-objective optimization problems.

关键词： evolutionary algorithms Large-scale optimization Multi-objective optimization Problem transformation

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Robust nonlinear control of permanent magnet synchronous motor drives: An evolutionary algorithm optimized passivity-based control approach with a high-order sliding mode observer

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Belkhier, Youcef Fredj, Siham Rashid, Haroon Benbouzid, Mohamed Univ Brest UMR CNRS 6027 F-29238 Brest France Univ Bejaia Fac Technol Lab Technol Ind & Informat LTII Bejaia 06000 Algeria

Permanent Magnet Synchronous Machines (PMSMs) have revolutionized motor design by replacing traditional components like rotor windings, brushes, and sliding contacts with permanent magnets. This innovation has significantly improved operational efficiency and reduced maintenance needs. However, controlling PMSMs remains challenging due to the changing dynamics of the machine over time and its sensitivity to different environmental conditions. To tackle these challenges, this study presents a novel nonlinear control approach called passivity-based control (PBC). Unlike conventional methods, PBC manages both the electrical and mechanical dynamics of the system, focusing on energy flow and dissipation to maintain stability. To make the control more robust, the approach combines a nonlinear observer and a high-order sliding mode controller (HSMC), which enhance the system's ability to handle disturbances and parameter changes. Additionally, the study uses Genetic Algorithm (GA) optimization to fine-tune the parameters of the PBC, observer, and HSMC. This optimization improves the motor's tracking accuracy and robustness against external disruptions. The result is a control framework that preserves the natural dynamics of PMSMs while improving their stability and performance. Experimental validation using the platform for real-time simulation (OPAL-RT) and real world on a PMSM using dSPACE DS1202 board demonstrates that this method outperforms existing techniques under a variety of operating conditions, highlighting its effectiveness and reliability.

关键词： evolutionary algorithms Nonlinear control Passivity-based control Nonlinear observer Permanent magnet synchronous machines

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Enhancing transparency of omics data analysis with the evolutionary Multi-Test Tree and Relative Expression

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Czajkowski, Marcin Jurczuk, Krzysztof Kretowski, Marek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

This paper presents a novel classification algorithm for multi-omics data, called evolutionary Multi-Test Tree with Relative Expression (EMTTree+RX). The innovation lies in the model's design, which integrates multi-test decision nodes with Relative Expression Analysis (RXA). Each decision node combines traditional univariate tests and top-scoring pair (TSP) comparisons, allowing the algorithm to capture complex relationships between features without relying solely on absolute values. This approach enables the proposed method to detect subtle patterns across various omics layers while maintaining a high level of interpretability, a feature crucial for clinical and bioinformatics applications. The tree structure is induced through evolutionary algorithms (EA), optimizing both the global architecture and local multi-test nodes to balance classification accuracy, test diversity, and feature cost. Applied to large-scale multi-omics datasets, where conventional decision tree methods often struggle with underfitting or overfitting, the proposed method consistently outperforms traditional models in terms of accuracy and transparency. This makes it a valuable tool for precision medicine and multi-modal data integration.

关键词： Multi-omics Decision trees Relative Expression Analysis evolutionary algorithms Top-scoring pair

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Coloring Dynamic Graphs With a Similarity and Pool-Based evolutionary Algorithm

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IEEE ACCESS 2025年 13卷 38054-38075页

作者： Terci, Gizem Sungu Boz, Betul Gebze Tech Univ Inst Informat Technol TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. The algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.

关键词： Heuristic algorithms Color evolutionary computation Social networking (online) Real-time systems Partitioning algorithms Adaptation models Metaheuristics Genetic operators Communication networks Dynamic graphs edge-dynamic graphs node-dynamic graphs graph coloring problem crossover operator evolutionary algorithms

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Dental X-Ray image enhancement using a novel evolutionary optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 142卷

作者： Yildirim, Brahim Bozkurt, Mustafa Hakan Kahraman, Hamdi Tolga Aras, Sefa Kron Teknol Akdeniz Mah TR-35210 Izmir Turkiye Karadeniz Tech Univ Technol Fac Software Engn TR-61080 Trabzon Turkiye Trabzon Teknokent Maveria Technol TR-61081 Trabzon Turkiye Karadeniz Tech Univ Artificial Intelligence & Data Engn Fac Engn TR-61080 Trabzon Turkiye

As an optimization problem, the main challenges in the enhancement of dental X-ray images are to perform the tasks of edge detection, noise removal and brightness adjustment in a precise and efficient manner. To overcome these challenges, designing an optimization algorithm that exhibits exploit-exploit behavior in accordance with the geometric structure of the search space of dental images is an important challenge that has not yet been realized. Shortcomings in this area lead to local solution traps and early convergence problems in image enhancement algorithms. This paper introduces the dFDB-LSHADE (dynamic fitness-distance balance-based achievement-history-based adaptive differential evolution with linear population size reduction) algorithm, which is designed for the enhancement of dental X-ray images according to the requirements of the search space of this problem and dynamically changes its exploitation-exploration capabilities. The proposed method is tested on a dataset of 120 periapical images, the most extensive experimental study in the literature. A total of 60 competing algorithms, 53 heuristics and 7 deterministic-based algorithms, were used in the experiments. In the study on dental images, the proposed algorithm has a better Friedman score than all competitors. According to the statistical analysis results obtained from the Wilcoxon pairwise test, the proposed dFDB-LSHADE was able to find better solutions for 23.3% of the images compared to its strongest competitor on the dental image set.

关键词： Dental x-ray image processing Meta-heuristic image improvement Dynamic fitness distance balance Linear population size reduction optimization Artificial intelligence in dentistry evolutionary algorithms

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Multi-objective evolutionary algorithm for the design of resilient OTN over DWDM networks

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OPTICAL FIBER TECHNOLOGY 2025年 93卷

作者： Silva, Jose C. Xavier, Andre V. S. Martins-Filho, Joaquim F. Bastos-Filho, Carmelo J. A. Chaves, Daniel A. R. Oliveira, Arysson S. Almeida Jr, Raul C. Araujo, Danilo R. B. Martins, Jose A. Univ Fed Pernambuco Av Arquitetura S-N BR-50740550 Recife PE Brazil Univ Pernambuco Rua Benfica 455 BR-50720001 Recife PE Brazil Univ Fed Rural Pernambuco Rua Dom Manoel Medeiros S-N BR-52171900 Recife PE Brazil Padtec SA Rua Dr Ricardo Benetton Martins 1000 BR-13086902 Campinas SP Brazil

Optical Transport Networks (OTN) arrived in the communications market bringing economic and operational benefits of virtualization. This technology works as a superior layer to the DWDM (Dense Wavelength Division Multiplexing) network, allowing more efficient resource usage. OTN approach decouples customers from DWDM interfaces, ensuring that optical links work more efficiently. This concept is carried out at OTN switches, allowing traffic to be aggregated at intermediate nodes and directed to routes that are being underutilized. Planning OTN over optical networks is a complex problem involving a new equipment structure and logical architecture. This problem has arisen due to academia and industry's interest in developing planning heuristics to reduce the cost of the network. This paper proposes an algorithm to perform OTN network planning to meet the services and their survivability requirements such as restoration and/or protection with the goal of reducing the network cost in terms of the number of OTN interfaces. We propose to use a multi-objective evolutionary algorithm to seek a solution that optimizes the project considering two conflicting decision variables: the number of used OTN interfaces and the average number of restored paths in case of double-link failures. The experiments showed that the proposed solution obtained fewer allocated interfaces than previous heuristic algorithms found in the literature.

关键词： evolutionary algorithms Metaheuristics Optical networks OTN Network survivability

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Personalized Indicator Based evolutionary Algorithm for Uncertain Constrained Many-Objective Optimization Problem With Interval Functions

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第3期37卷

作者： Wen, Jie Wang, Qian Dong, Haozhe Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Adv Control & Equipment Intelligenc Taiyuan Shanxi Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Shanxi Peoples R China

In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many-objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly in balancing solution convergence, diversity, feasibility, and uncertainty. To address these challenges, a personalized indicator-based evolutionary algorithm (PI-ICMaOEA) specifically designed for ICMaOPs is proposed. The PI-ICMaOEA integrates a comprehensive quality indicator that encapsulates convergence, diversity, uncertainty, and feasibility factors, converting multiple objectives in high-dimensional search spaces into a single evaluative metric. Each factor's weight is personalized assigned based on individual performance, objective dimension, and the evolving conditions of the population. By prioritizing individuals with excellent indicator values for mating and environmental selection, PI-ICMaOEA effectively enhances selection pressure in high-dimensional spaces. Comparative simulations demonstrate that PI-ICMaOEA is highly competitive, offering a robust solution for balancing convergence, diversity, uncertainty, and feasibility in ICMaOPs.

关键词： constraint handling evolutionary algorithms interval optimization many-objective optimization

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ECODI: a novel evolutionary coreset distillation with LLM-assisted fitness evaluation for encrypted network traffics

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-19页

作者： Tran, Hai-Anh Tong, Van Hanoi Univ Sci & Technol Sch Informat & Commun Technol 1 Dai Co Viet Hanoi 10000 Vietnam

Nowadays, machine learning-based methods have become essential for classifying network data flows under encryption, as traditional deep packet inspection is ineffective due to encryption protocols like HTTPS and QUIC, which now cover over 85% of Internet traffic. However, the scale of modern Internet traffic introduces new challenges, particularly the massive size of datasets required for training these models. Handling such large datasets results in excessive computational costs, prompting the need for data condensation techniques that reduce dataset size without sacrificing performance. In this paper, we propose a novel evolutionary Coreset Distillation method for network traffic classification. Our approach, named ECODI, combines the power of evolutionary algorithms with Large Language Models (LLMs) to condense large datasets into smaller, representative coresets. We employ LLMs to generate high-level embeddings that guide the evolutionary algorithm in selecting coresets, thus preserving the most important information while reducing the dataset size. Additionally, we introduce a gradient-based forgetting mechanism to further refine the coreset by eliminating redundant or low-impact data points. The extensive experiments demonstrate that ECODI outperforms both traditional methods (Random Sampling, K-Center, and Herding) and recent evolutionary approaches (EVA and DEvS) in achieving high classification performance with reduced dataset sizes. Notably, ECODI achieves a fitness score of 0.94 in as few as 10 generations, offering substantial improvements in terms of both convergence speed and final classification accuracy compared to EVA and DEvS.

关键词： Dataset condensation Coreset distillation evolutionary algorithms LLM

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Automatic evolutionary design of quantum rule-based systems and applications to quantum reinforcement learning

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QUANTUM INFORMATION PROCESSING 2024年第5期23卷 179-179页

作者： Cuellar, Manuel P. Pegalajar, M. C. Cano, C. Univ Granada Dept Comp Sci & Artificial Intelligence C Periodista Daniel Saucedo Aranda S-N Granada 18014 Andalucia Spain

Explainable artificial intelligence is a research topic whose relevance has increased in recent years, especially with the advent of large machine learning models. However, very few attempts have been proposed to improve interpretability in the case of quantum artificial intelligence, and many existing quantum machine learning models in the literature can be considered almost as black boxes. In this article, we argue that an appropriate semantic interpretation of a given quantum circuit that solves a problem can be of interest to the user not only to certify the correct behavior of the learned model, but also to obtain a deeper insight into the problem at hand and its solution. We focus on decision-making problems that can be formulated as classification tasks and propose a method for learning quantum rule-based systems to solve them using evolutionary optimization algorithms. The approach is tested to learn rules that solve control and decision-making tasks in reinforcement learning environments, to provide interpretable agent policies that help to understand the internal dynamics of an unknown environment. Our results conclude that the learned policies are not only highly explainable, but can also help detect non-relevant features of problems and produce a minimal set of rules.

关键词： Quantum rule-based system Quantum reinforcement learning Quantum artificial intelligence Explainable artificial intelligence evolutionary algorithms

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