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

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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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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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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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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Proximal evolutionary strategy: improving deep reinforcement learning through evolutionary policy optimization

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MEMETIC COMPUTING 2024年第3期16卷 445-466页

作者： Peng, Yiming Chen, Gang Zhang, Mengjie Xue, Bing Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Sch Engn & Comp Sci Wellington 6140 New Zealand

evolutionary algorithms (EAs), including evolutionary Strategies (ES) and Genetic algorithms (GAs), have been widely accepted as competitive alternatives to Policy Gradient techniques for Deep Reinforcement Learning (DRL). However, they remain eclipsed by cutting-edge DRL algorithms in terms of time efficiency, sample complexity, and learning effectiveness. In this paper, aiming at advancing evolutionary DRL research, we develop an evolutionary policy optimization algorithm with three key technical improvements. First, we design an efficient layer-wise strategy for training DNNs through Covariance Matrix Adaptation evolutionary Strategies (CMA-ES) in a highly scalable manner. Second, we establish a surrogate model based on proximal performance lower bound for fitness evaluations with low sample complexity. Third, we embed a gradient-based local search technique within the evolutionary policy optimization process to further improve the learning effectiveness. The three technical innovations jointly forge a new EA for DRL method named Proximal evolutionary Strategies (PES). Our experiments on ten continuous control problems show that PES with layer-wise training can be more computationally efficient than CMA-ES;our surrogate model can remarkably reduce the sample complexity of PES in comparison to latest EAs for DRL including CMA-ES, OpenAI-ES, and Uber-GA;PES with gradient-based local search can significantly outperform several promising DRL algorithms including TRPO, AKCTR, PPO, OpenAI-ES, and Uber-GA.

关键词： evolutionary algorithms Covariance matrix adaptation evolutionary strategy NeuroEvolution Policy gradient search Deep reinforcement learning

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A Two-Phase Kriging-Assisted evolutionary Algorithm for Expensive Constrained Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第8期54卷 4579-4591页

作者： Zhang, Zhiyao Wang, Yong Liu, Jiao Sun, Guangyong Tang, Ke Cent South Univ Sch Automat Changsha 410083 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha 410082 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

This article devises a two-phase Kriging-assisted evolutionary algorithm (named TEA) to tackle expensive constrained multiobjective optimization problems (CMOPs). In the first phase, only objectives are considered, which can help the population to cross infeasible obstacles and to evolve toward the unconstrained Pareto front. Since the unconstrained Pareto front is in front of the feasible region in the objective space, the first phase can find some feasible solutions during the evolution. In the second phase, both objectives and constraints are considered. In this article, we also propose two transition conditions to judge whether the search should be switched from the first phase to the second phase, by making use of the candidates evaluated by the original objectives and constraints in the first phase. These two transition conditions aim at maintaining some high-quality feasible solutions when the first phase ends, which is able to motivate the population to converge toward the constrained Pareto front with good diversity in the second phase. Furthermore, in both phases, we design a new Pareto dominance relationship (called PDPD) by incorporating the probability distribution information derived from the Kriging models. PDPD is further generalized to handle constraints in expensive CMOPs, Constrained PDPD (CPDPD), which provides high credibility for the comparison between two individuals with respect to both objectives and constraints. Finally, three benchmark test suites and a real-world application confirm the superiority of TEA.

关键词： evolutionary algorithms expensive constrained multiobjective optimization problems Kriging model Pareto dominance

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Predicting molecular crystals of polynitrogen (N 6 ) structures with cage-like geometries using ab initio evolutionary algorithm

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CHEMICAL PHYSICS LETTERS 2024年 844卷

作者： Freitas, W. D. Mazzoni, M. S. C. Matos, M. J. S. Galvao, B. R. L. CEFET MG Ctr Fed Educ Tecnol Minas Gerais Ave Amazonas 5253 BR-30421169 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Fis Inst Ciencias Exatas Ave Antonio Carlos 6627 Belo Horizonte MG Brazil Univ Fed Ouro Preto Inst Ciencias Exatas & Biol Dept Fis Campus Univ Morro Cruzeiro BR-35400000 Ouro Preto MG Brazil

In this work, we employed Density Functional Theory calculations combined with search techniques based on evolutionary algorithms to predict and characterize crystalline structures composed of nitrogen (N 6 ) cagelike molecules. We found stable molecular crystals and a rich phenomenology associated with their behavior under pressure, including atomic rebonding and semiconductor -metal transitions. This investigation resides in the context of high -energy -density materials, since molecular species containing only nitrogen atoms tend to dissociate into N 2 molecules, releasing large amounts of energy.

关键词： Polynitrogen DFT evolutionary algorithms

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Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining evolutionary Algorithm, Transfer Learning, and Model Compression

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2519-2547页

作者： Hassen Louati Ali Louati Elham Kariri Slim Bechikh College of Information Technology Kingdom UniversityRiffa40434Bahrain SMART Lab University of TunisISGTunisTunisia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease *** promising avenue involves the use of chest X-Rays,which are commonly utilized in *** fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic ***,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data *** tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image *** approach accurately classifies radiography images and detects potential chest abnormalities and infections,including ***,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting *** method can help reduce the amount of labeled data required for the task and enhance the overall performance of the *** have validated our method via a series of experiments against state-of-the-art architectures.

关键词： Computer-aided diagnosis deep learning evolutionary algorithms deep compression transfer learning

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Online regularity learning-based evolutionary multiobjective optimization and its application in aircraft trajectory planning

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年 1-29页

作者： Lu, Yulan Wang, Haoyue Yu, Jiamin Sun, Xin Si, Xinhui Zhang, Hu Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China CASIC Res Inst Intelligent Decis Engn Beijing 100074 Peoples R China

A continuous m-objective optimization problem exhibits a regularity property under mild conditions, such that the Pareto set of the multiobjective optimization problem (MOP) forms an (m-1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(m-1)$$\end{document}-dimensional piecewise continuous manifold. Leveraging this regularity in the design of multiobjective evolutionary algorithms can be advantageous. In this paper, we propose an online regularity learning-based evolutionary multiobjective optimization (OCEMO) algorithm. Given that the data generated by evolutionary algorithms are typically non-stationary and independent, OCEMO integrates an online clustering approach directly into the evolutionary process at the operator level. After each generation of evolution, a clustering iteration is performed to gradually uncover the regular structure of the Pareto set. The learned neighborhood relationships among solutions are then used to serve the mating selection and guide the search process within the algorithm. Experimental results demonstrate that OCEMO significantly outperforms several state-of-the-art multiobjective evolutionary algorithms on complex test suites and in a real-world application of aircraft trajectory planning.

关键词： evolutionary algorithms Multiobjective optimization Regularity property of multiobjective optimization problem Online agglomerative clustering Aircraft trajectory planning

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