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A Review of the Evolution of Multi-Objective Evolutionary Algorithms 认领 引用 被引量：1
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作者 Thomas Hanne Mohammad Jahani Moghaddam 《Computers, Materials & Continua》 SCIE EI 2025年第12期4203-4236,共34页
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp... Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential. 展开更多
关键词 Multi-objective optimization evolutionary algorithms Pareto-based selection decomposition-based methods advanced analytics
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Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms:An Extensive Review 认领 引用
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作者 Siti Ramadhani Lestari Handayani +4 位作者 Theam Foo Ng Sumayyah Dzulkifly Roziana Ariffin Haldi Budiman Shir Li Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2711-2765,共55页
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati... In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency. 展开更多
关键词 Feature selection(FS) gene expression profile(GEP) cancer classification evolutionary algorithms(EAs) dynamic-length chromosome
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Many-objective evolutionary algorithms based on reference-point-selection strategy for application in reactor radiation-shielding design 认领 引用
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作者 Cheng-Wei Liu Ai-Kou Sun +4 位作者 Ji-Chong Lei Hong-Yu Qu Chao Yang Tao Yu Zhen-Ping Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第6期201-215,共15页
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct... In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types. 展开更多
关键词 Many-objective optimization problem Evolutionary algorithm Radiation-shielding design Reference-point-selection strategy
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MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS 认领 引用 被引量：3
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作者 LiJun LiuLijun FengZhenping 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第3期389-393,共5页
Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the th... Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design. 展开更多
关键词 Centrifugal impeller Navier-Stokes solver Evolutionary algorithms Multiobjective optimization Design
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Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms 认领 引用 被引量：2
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作者 Haykel Marouani Omar Al-mutiri 《Computers, Materials & Continua》 SCIE EI 2022年第4期537-571,共35页
The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humani... The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humanity.In this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems.Results from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance.However,this improvement has been difficult to achieve for high-reliability applications.Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper. 展开更多
关键词 Reliability redundancy evolutionary algorithms
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Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping 认领 引用 被引量：4
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作者 李枚毅 蔡自兴 《Journal of Central South University of Technology》 2006年第5期529-535,共7页
Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were de... Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms. 展开更多
关键词 immune evolutionary algorithms simultaneous localization and mapping domain knowledge
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Time Complexity of Evolutionary Algorithms for Combinatorial Optimization:A Decade of Results 认领 引用 被引量：14
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作者 Pietro S.Oliveto 《International Journal of Automation and computing》 2007年第3期281-293,共13页
Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems.... Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered. 展开更多
关键词 Evolutionary algorithms computational complexity combinatorial optimization evolutionary computation theory.
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CONVERGENCE RATES FOR A CLASS OF EVOLUTIONARY ALGORITHMS WITH ELITIST STRATEGY 认领 引用 被引量：1
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作者 Ding Lizin Kang Lishan 《Acta Mathematica Scientia》 SCIE 2001年第4期531-540,共10页
This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain condition... This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy,the authors obtain the convergence rates of the exponential order.Furthermore,they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies. 展开更多
关键词 convergence rate Markov chain Banach algebra genetic operator elitist selection evolutionary algorithms
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Hierarchical Evolutionary Algorithms and Its Application in Transonic Airfoil Optimization in Aerodynamics 认领 引用
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作者 王江峰 伍贻兆 《Chinese Journal of Aeronautics》 EI CAS 2003年第1期1-6,共6页
Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization p... Hierarchical evolutionary algorithms based on genetic algorithms (GAs) and Nash strategy of game theory are proposed to accelerate the optimization process and implemented in transonic aerodynamic shape optimization problems Inspired from the natural evolution history that different periods with certain environments have different criteria for the evaluations of individuals’ fitness, a hierarchical fidelity model is introduced to reach high optimization efficiency The shape of an NACA0012 based airfoil is optimized in maximizing the lift coefficient under a given transonic flow condition Optimized results are presented and compared with the single model results and traditional GA 展开更多
关键词 transonic flow aerodynamic optimization finite element method unstructured grid hierarchical fidelity models evolutionary algorithms
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Selection of optimal land uses for the reclamation of surface mines by using evolutionary algorithms 认领 引用 被引量：3
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作者 Palogos Ioannis Galetakis Michael +1 位作者 Roumpos Christos Pavloudakis Francis 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2017年第3期491-498,共8页
A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related t... A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure. 展开更多
关键词 Reclamation Land uses Optimization Evolutionary algorithms Desirability functions
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Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms 认领 引用 被引量：3
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作者 He-Xing Yin Jia-Bao Guan +1 位作者 Shun-Qiang Tian Ji-Ke Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第10期20-35,共16页
Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate wh... Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate when the optimization objectives for an accelerator are equal to or greater than four. Recently, many-objective evolutionary algorithms(MaOEAs)that can solve problems with four or more optimization objectives have received extensive attention. In this study, two diffraction-limited storage ring(DLSR) lattices of the Extremely Brilliant Source(ESRF-EBS) type with different energies were designed and optimized using three MaOEAs and a widely used MOEA. The initial population was found to have a significant impact on the performance of the algorithms and was carefully studied. The performances of the four algorithms were compared, and the results demonstrated that the grid-based evolutionary algorithm(GrEA) had the best performance.Ma OEAs were applied in many-objective optimization of DLSR lattices for the first time, and lattices with natural emittances of 116 and 23 pm·rad were obtained at energies of 2 and 6 GeV, respectively, both with reasonable dynamic aperture and local momentum aperture(LMA). This work provides a valuable reference for future many-objective optimization of DLSRs. 展开更多
关键词 Storage ring lattices Many-objective evolutionary algorithms GrEA algorithm NSGA
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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems 认领 引用 被引量：2
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作者 Sheng Qi Rui Wang +3 位作者 Tao Zhang Weixiong Huang Fan Yu Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1786-1801,共16页
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to tr... Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges. 展开更多
关键词 Evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.
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Managing Software Testing Technical Debt Using Evolutionary Algorithms 认领 引用 被引量：1
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作者 Muhammad Abid Jamil Mohamed K.Nour 《Computers, Materials & Continua》 SCIE EI 2022年第10期735-747,共13页
Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negati... Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process. 展开更多
关键词 Technical debt software testing optimization large scale agile projects evolutionary algorithms multiobjective optimization indicatorbased evolutionary algorithm(IBEA) pareto front
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A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts 认领 引用 被引量：53
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作者 Yicun Hua Qiqi Liu +1 位作者 Kuangrong Hao Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第2期303-318,I0001-I0004,共16页
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remed... Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested. 展开更多
关键词 Evolutionary algorithm machine learning multi-objective optimization problems(MOPs) irregular Pareto fronts
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Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis 认领 引用 被引量：6
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作者 石磊 姚平经 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2001年第2期173-178,共6页
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes... Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis. 展开更多
关键词 multi-objective programming multi-objective evolutionary algorithm steady-state non-dominated sorting genetic algorithm process synthesis
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Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism 认领 引用 被引量：3
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作者 何宏 钱锋 《Journal of Donghua University(English Edition)》 EI CAS 2007年第1期141-145,共5页
Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation ... Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way. 展开更多
关键词 evolutionary algorithm immune network adaptation stimulation level.
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Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues 认领 引用 被引量：1
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作者 LIAO Lingxia Victor C.M.Leung LAI Chin-Feng 《ZTE Communications》 2017年第3期20-36,共17页
A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o... A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs. 展开更多
关键词 SDN evolutionary algorithms Genetic Algorithms Particle Swarm Optimization Ant Colony Optimization Simulated Annealing
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Two Aspects of Evolutionary Algorithms 认领 引用 被引量：3
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作者 Zbigniew Michalewicz Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA, and Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237 Warsaw, Poland 《Wuhan University Journal of Natural Sciences》 EI CAS 2000年第4期413-424,共12页
In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After intr... In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints. 展开更多
关键词 Key words problem solving evolutionary algorithms heuristics constraint handling adaptation
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When Large Language Models Meet Evolutionary Algorithms:Potential Enhancements and Challenges 认领 引用
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作者 Chao Wang Jiaxuan Zhao +3 位作者 Licheng Jiao Lingling Li Fang Liu Shuyuan Yang 《Research》 SCIE EI CSCD 2026年第1期68-81,共14页
Pre-trained large language models(LLMs)exhibit powerful capabilities for generating natural text.Evolutionary algorithms(EAs)can discover diverse solutions to complex real-world problems.Motivated by the common collec... Pre-trained large language models(LLMs)exhibit powerful capabilities for generating natural text.Evolutionary algorithms(EAs)can discover diverse solutions to complex real-world problems.Motivated by the common collective and directionality of text generation and evolution,this paper first illustrates the conceptual parallels between LLMs and EAs at a micro level,which includes multiple one-to-one key characteristics:token representation and individual representation,position encoding and fitness shaping,position embedding and selection,Transformers block and reproduction,and model training and parameter adaptation.These parallels highlight potential opportunities for technical advancements in both LLMs and EAs.Subsequently,we analyze existing interdisciplinary research from a macro perspective to uncover critical challenges,with a particular focus on evolutionary fine-tuning and LLM-enhanced EAs.These analyses not only provide insights into the evolutionary mechanisms behind LLMs but also offer potential directions for enhancing the capabilities of artificial agents. 展开更多
关键词 fitness shapingposi large language models llms exhibit evolutionary algorithms token representation generating natural textevolutionary algorithms eas can large language models individual representation text generation
AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS 认领 引用 被引量：1
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作者 Ding Lixin Kang Lishan +1 位作者 Chen Yupin Zhou Shaoquan 《Wuhan University Journal of Natural Sciences》 EI CAS 1998年第1期31-31,共1页
Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstructio... Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured. 展开更多
关键词 global random search evolutionary algorithms weak convergence genetic algorithms
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