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evolutionary computation in the Era of Large Language Model: Survey and Roadmap

IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 534-554页

作者： Wu, Xingyu Wu, Sheng-Hao Wu, Jibin Feng, Liang Tan, Kay Chen Hong Kong Polytech Univ Dept Data Sci & Artificial Intelligence Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride toward artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under closed box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this article provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: 1) LLM-enhanced EA and 2) EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this article provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence. We have created a GitHub repository to index the relevant papers: https://***/wuxingyu-ai/LLM4EC.

关键词： Optimization Closed box Reviews evolutionary computation Codes Search problems Collaboration Surveys Software engineering Prompt engineering Algorithm generation evolutionary algorithm (EA) large language model (LLM) neural architecture search (NAS) optimization problem prompt engineering

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Competition of tribes and cooperation of members algorithm: An evolutionary computation approach for model free optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Chen, Zuyan Li, Shuai Khan, Ameer Tamoor Mirjalili, Seyedali Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland VTT Tech Res Ctr Finland Oulu 90590 Finland Univ Copenhagen Dept Plant & Environm Sci DK-1172 Copenhagen Denmark Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Metaheuristic algorithms solve optimization problems mostly by imitating behaviors observed in nature. Over time, these algorithms have proven to be very effective in solving complex optimization problems. Due to the rising complexity and scale of practical engineering problems, numerous metaheuristic algorithms have been developed recently and applied in various fields. In response to this need, researchers continue to explore novel approaches inspired by natural and social phenomena. Inspired by the competition among ancient tribes and their cooperative behavior, this paper proposes a meta-heuristic called the Competition of Tribes and Cooperation of Members Algorithm (CTCM). Experiments are conducted on 23 benchmark test functions and comprehensively compared with other state-of-the-art algorithms, including particle swarm optimization (PSO), grey wolf optimizer (GWO), sparrow search algorithm (SSA), egret swarm optimization (ESOA), beetle antennae search (BAS) and whale optimization (WOA). The standard deviation and average, as well as statistical tests are utilized to compare the performance of each algorithm, which demonstrates that CTCM is superior in the majority of problems. In addition, the results of Wilcoxon and Friedman rank tests show that the CTCM achieves the first place in all categories of problems. The results indicate that CTCM possesses strong global optimization search capability and stability, and has faster convergence speed. The paper also considers solving practical engineering optimization problems as proof-of-concept case studies, in which CTCM achieves all the optimal solutions for each engineering problem.

关键词： evolutionary computation Competition of tribes and cooperation of members algorithm Meta-heuristics Swarm intelligence Engineering optimization Optimization Algorithm

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A filter-wrapper model for high-dimensional feature selection based on evolutionary computation

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APPLIED INTELLIGENCE 2025年第7期55卷 1-13页

作者： Hu, Pei Zhu, Jiulong Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Henan Peoples R China Nanyang Inst Technol FanLi Business Sch Nanyang 473004 Henan Peoples R China

In machine learning, feature selection plays an important role in improving prediction accuracy and reducing time complexity. This paper proposes a filter-wrapper model to obtain a feature subset from high-dimensional data in a short time. Firstly, features are ranked by information gain and Fisher Score. Secondly, the feature search is realized by binary evolutionary computation based on wrapper. To avoid wasting a lot of searches on low-ranked features, an adaptive feature selection strategy is adopted to guide population search and position update. Finally, a learning strategy is proposed, in which learners study from exemplars and complete position update, and the exemplars are constituted by optimal solutions to balance exploration and exploitation. To demonstrate the effectiveness and efficiency of the proposed model, three binary evolutionary computations, including particle swarm optimization, grey wolf optimizer, and fish migration optimization, are applied to the model, and they present excellent performance in high-dimensional data sets.

关键词： Feature selection Filter-wrapper Adaptive High-dimensional evolutionary computation

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An Intelligent Ride-Sharing Recommendation Method Based on Graph Neural Network and evolutionary computation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 569-578页

作者： Zhou, Qian Wu, Jiayang Dai, Hua Yang, Geng Zhang, Yanchun Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Univ Victoria Inst Sustainable Ind & Liveable Cities Melbourne Vic 3011 Australia

This research is dedicated to addressing user recommendation matching and multi-objective optimization problems in ride-sharing services. For addressing the challenge of node classification in social networks, the Graph Attention Network with Opinion Dynamics (OD-GAT) is proposed. This model combines opinion dynamics and attention mechanism, which can make full use of multi-dimensional information for social relationship reasoning, and at the same time simulate the influence of individuals by other objects in the group, realize more accurate prediction and reasoning of social relationships, improved service quality and ride-sharing safety. To address the intricate task of balancing multiple objectives, including average detour cost, average response rate, and average user similarity rate, we introduce a novel evolutionary computation method for optimizing ride-sharing scenarios. This approach tackles the dynamic ride-sharing matching problem by emphasizing human factors in the optimization goals, successfully overcoming challenges related to local optima and convergence. Experimental validation confirms the effectiveness of OD-GAT in feature extraction and classification, showcasing the method's fastest convergence speed and global optimum achievement across three key metrics.

关键词： Graph attention network opinion dynamics social network analysis evolutionary computation Graph attention network opinion dynamics social network analysis evolutionary computation

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Prediction of Patient Visits for Skin Diseases through Enhanced evolutionary computation and Ensemble Learning

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JOURNAL OF MEDICAL SYSTEMS 2025年第1期49卷 1-12页

作者： Leng, Wenting Yang, Chenglin Kou, Menggang Zhang, Kequan Liu, Xinyue Lanzhou Univ Hosp & Clin Med Sch 2 Lanzhou 730000 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Lanzhou Univ Sch Management Lanzhou 730000 Peoples R China

Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Based on machine learning technology and ensemble learning theory, this study integrates four neural network models to construct an optimal prediction model for daily outpatient visits related to skin diseases. To address the issue of local optima entrapment in sand cat swarm optimization (SCSO), an enhanced SCSO is proposed by incorporating the chaotic mapping, the spiral search strategy, and the sparrow warning mechanism. The enhanced SCSO is then utilized to optimize two critical parameters of variational mode decomposition, enabling the extraction of periodic patterns from the skin disease time series. Finally, the enhanced SCSO is applied again to determine the optimal weights for the ensemble model, thereby achieving optimal fusion predictions. We utilized ten years of outpatient data from the dermatology department of a hospital in China, and selected acne, the most prevalent skin condition in the region, as a case study. Experimental results demonstrate that the proposed model effectively combines the strengths of each module, achieving an root mean squared error (RMSE) of 4.43 and an R-squared (R2) of 0.98. Compared to individual models, the RMSE and R2 are improved by 79.69% and 36.97%, respectively, effectively overcoming the limitations of single-model approaches. This research provides valuable insights for leveraging medical time series data and optimizing healthcare resource allocation.

关键词： Outpatient prediction Signal processing evolutionary computation Ensemble learning

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Learning-infused optimization for evolutionary computation

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SWARM AND evolutionary computation 2025年 95卷

作者： Bian, Kun Zhang, Juntao Han, Hong Zhou, Jun Sun, Yifei Cheng, Shi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China AMS Inst Syst Engn Beijing 100141 Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

evolutionary computation is a class of meta-heuristic algorithm that mimics the process of biological evolution, utilizing information exchange among individuals in the population to iteratively search for optimal solutions. During the evolutionary process, a substantial amount of data is generated, from which valuable evolutionary information can be extracted to assist the algorithm to evolve in a more effective direction. Additionally, neural networks excel at extracting knowledge from data. Motivated by this, we propose a learning-infused optimization (LIO) framework that employs neural networks to learn the evolutionary processes of the algorithms and extract synthesis patterns from the valuable evolutionary information. These synthesis patterns possess excellent generalizability and effectiveness, guiding the algorithm towards better solutions on the original problems and enabling transfer evolution ability, which can improve the performance of the algorithm on new problems. The LIO framework is applied to various algorithms. Experimental results demonstrate that the synthesis patterns extracted from the CEC14 problems not only guide the evolution of the algorithms towards better solutions on the original problems, but also significantly improve the performance of the algorithms on the CEC17 problems.

关键词： evolutionary computation Deep learning Learning-infused optimization Synthesis patterns

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evolutionary computation for Large-scale Multi-objective Optimization: A Decade of Progresses

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International Journal of Automation and computing 2021年第2期18卷 155-169页

作者： Wen-Jing Hong Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management Science University of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence Guangzhou 510515China

Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world *** evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision *** recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision *** a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past *** paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two *** the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision *** the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based *** future research directions are also discussed.

关键词： Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability

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evolutionary computation for Community Detection in Networks: A Review

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IEEE TRANSACTIONS ON evolutionary computation 2018年第3期22卷 464-483页

作者： Pizzuti, Clara Natl Res Council Italy Inst High Performance Comp & Networking I-87036 Arcavacata Di Rende Italy

In today's world, the interconnections among objects in many domains are often modeled as networks, with nodes representing the objects and edges the existing relationships among them. A key feature of complex networks is the tendency of entities to group together to form communities. The detection of communities has been receiving a great deal of interest by researchers. In fact, the knowledge of how objects organize allows a better understanding of a network, and gives a deeper insight of interesting characteristics, that could not be caught if considering the network as a whole. In the last decade, evolutionary computation techniques have given a significant contribution in this context. The aim of this paper is to present the approaches based on evolutionary computation to uncover community structure. Especially, the representation schemes with the genetic operators apt for them are described, and the most popular fitness functions employed by the methods are discussed. The survey covers the most recent proposals optimizing either a single objective or multiple objectives for different types of network models, such as signed, dynamic, and multidimensional.

关键词： Community detection complex networks evolutionary computation multiobjective optimization single objective optimization

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evolutionary computation in Action: Hyperdimensional Deep Embedding Spaces of Gigapixel Pathology Images

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IEEE TRANSACTIONS ON evolutionary computation 2023年第1期27卷 52-66页

作者： Bidgoli, Azam Asilian Rahnamayan, Shahryar Dehkharghanian, Taher Riasatian, Abtin Tizhoosh, Hamid R. Ontario Tech Univ Nat Inspired Computat Intelligence Lab Oshawa ON L1G 0C5 Canada McMaster Univ Dept Pathol & Mol Med Hamilton ON L8S 4L8 Canada Univ Waterloo Lab Knowledge Inference Med Image Anal Waterloo ON N2L 3G1 Canada Mayo Clin Dept Artificial Intelligence & Informat Rochester MN 55905 USA

One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visualization and interpretability of pathology results in a postpandemic world. In this article, we introduce a new evolutionary approach for WSI representation based on large-scale multiobjective optimization (LSMOP) of deep embeddings. We start with patch-based sampling to feed KimiaNet, a histopathology-specialized deep network, and to extract a multitude of feature vectors. Coarse multiobjective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features. In the second stage, the frequent features histogram (FFH), a novel WSI representation, is constructed by multiple runs of coarse LSMOP. Fine evolutionary feature selection is then applied to find a compact (short-length) feature vector based on the FFH and contributes to a more robust deep-learning approach to digital pathology supported by the stochastic power of evolutionary algorithms. We validate the proposed schemes using The Cancer Genome Atlas (TCGA) images in terms of WSI representation, classification accuracy, and feature quality. Furthermore, a novel decision space for multicriteria decision making in the LSMOP field is introduced. Finally, a patch-level visualization approach is proposed to increase the interpretability of deep features. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI (almost 14 000 times smaller than the original feature vectors) with 8% higher accuracy compared to the codes provided by the state-of-the-art methods

关键词： Feature extraction Optimization evolutionary computation Data mining Histopathology Cancer Task analysis Deep neural network (DNN) digital pathology dimension reduction evolutionary computation (EC) feature selection image representation innovization large-scale multiobjective optimization (LSMOP) transfer learning

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evolutionary computation on Programmable Robust IIR Filter Pole-Placement Design

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2011年第4期60卷 1469-1479页

作者： Pan, Shing-Tai Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan

This paper explores the pole-placement design problem of a robust stable infinite-impulse-response (IIR) filter to attenuate or eliminate the undesired measurement noise and proposes a strategy based on an adaptive differential evolution (ADE) algorithm to design a filter. The results are compared to the results of other popular evolutionary algorithms, e. g., particle swarm optimization (PSO), genetic algorithm (GA), and improved genetic algorithm (IGA). The stability robustness for an IIR filter will be achieved by placing all poles inside a disk D(alpha, r) contained in the unit disk, in which alpha is the center, and r is the radius of the disk. This investigation first uses a robust stability criterion, called the D(alpha, r)-stability criterion, to ensure that digital filter poles lie inside a disk D(alpha, r). The proposed strategy checks the criterion during differential evolution (DE) and adaptively adjusts the DE parameters, depending on the current DE performance. This paper also introduces two design examples of a bandpass IIR filter and a low-pass IIR filter for the measurement of a speech signal. These examples show that the proposed strategy performance based on the proposed ADE is better than designs based on PSO, GA, and IGA. Finally, this paper implements an IIR filter on the field-programmable gate array (FPGA) chip to verify the designed filter performance in practical electronic devices and uses speech signals as an input signal to the FPGA chip to verify that the measurement noise of the speech signal is attenuated by the designed IIR filter.

关键词： evolutionary computation field-programmable gate array (FPGA) chip pole-placement design robustly stable infinite-impulse-response (IIR) filter

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