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evolutionary algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges

IEEE ACCESS 2025年 13卷 16649-16670页

作者： Ur Rehman, Junaid Ulum, Muhammad Shohibul Shaffar, Abdurrahman Wachid Hakim, Amirul Adlil Mujirin, Zaid Abdullah, Zaid Al-Hraishawi, Hayder Chatzinotas, Symeon Shin, Hyundong King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg City Luxembourg King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia Kyung Hee Univ Dept Elect & Informat Convergence Engn Yongin 17104 Gyeonggi Do South Korea Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.

关键词： Quantum computing evolutionary computation Qubit Quantum mechanics Metaheuristics Heuristic algorithms Hardware Terminology Computers Recommender systems evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing

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Activation Function-Assisted Objective Space Mapping to Enhance evolutionary algorithms for Large-Scale Many-Objective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 183-195页

作者： Deng, Qi Kang, Qi Zhou, MengChu Wang, Xiaoling Albeshri, Aiiad Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China New Jersey Inst Technol ECE Dept Newark NJ 07102 USA King Abdulaziz Univ Dept Comp Sci Jeddah 21481 Saudi Arabia

Large-scale many-objective optimization problems (LSMaOPs) pose great difficulties for traditional evolutionary algorithms due to their slow search for Pareto-optimal solutions in huge decision space and struggle to balance diversity and convergence among numerous locally optimal solutions. An objective space linear inverse mapping method has successfully achieved great saving in execution time in solving LSMaOPs. Linear mapping is a fast and straightforward way, but fails to characterize a complex functional relationship. If we can enhance the expressive capacity of a mapping model, and further obtain a more general function approximator, can the evolutionary search based on objective space mapping be more efficient? To answer this interesting question, this work proposes to employ nonlinear activation functions widely used in neural networks so as to enhance the efficiency of objective space inverse mapping, thus efficiently generating excellent offspring population. A new evolutionary optimization framework based on decision variable analysis is proposed to solve LSMaOPs. In order to demonstrate its performance, this work carries out empirical experiments involving massive decision variables and many objectives. Experimental results prove its superiority over some representative and updated ones.

关键词： Activation function evolutionary algorithms large-scale many-objective optimization objective space mapping

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Discovery of novel silicon allotropes with optimized band gaps to enhance solar cell efficiency through evolutionary algorithms and machine learning

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COMPUTATIONAL MATERIALS SCIENCE 2025年 246卷

作者： Yaghoobi, Mostafa Alaei, Mojtaba Shirazi, Mahtab de Gironcoli, Stefano de Gironcoli, Stefano Isfahan Univ Technol Dept Phys Esfahan 84156 83111 Iran Skolkovo Inst Sci & Technol Bolshoy Blvd 30Bld 1 Moscow 121205 Russia Scuola Int Super Studi Avanzati Trieste Italy Ist Officina Materiali CNR IOM DEMOCRITOS Trieste Italy

In the pursuit of advancing solar energy technologies, this study presents 20 direct and quasi-direct band gap silicon crystalline semiconductors that satisfy the Shockley-Queisser limit, a benchmark for solar cell efficiency. Employing two evolutionary algorithm-based searches, we optimize structures and calculate fitness function using the DFTB method and Gaussian approximation potential. Following the preselection of structures based on energy considerations, we further optimize them using PBEsol DFT. Subsequently, we screen the structures based on their band gap, employing a DFTB method tailored for band gap calculation of silicon crystals. To ensure accurate band gap determination, we employ HSE and GW methods. To validate the structural stability, we employ phonon analysis via linear regression algorithm applied to PBEsol DFT data. Significantly, the structures unveiled in this study are of great importance due to their proven stability from both mechanical and dynamic perspectives. Furthermore, the ductility and low density of certain structures enhance their potential application. We examine the optical properties by studying the imaginary part of the dielectric function by solving the Bethe-Salpeter Equation on top of GW approximation. By calculating the SLME, we achieve an efficiency of 32.7% for Si22 at a thickness of 500 nm. Moreover, the study harnesses various machine learning algorithms to develop a predictive model for the band gap energy of these silicon structures. Input data for machine learning models are derived from structural MBTR and SOAP descriptors, as well as DFT outputs. Notably, the results reveal that features extracted from DFT outperform the MBTR and SOAP descriptors.

关键词： evolutionary algorithms Machine learning Solar cell Density Functional Theory Si crystals Density functional Tight Binding GW Spectroscopic limited maximum efficiency Dielectric function

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Meta-Black-Box optimization for evolutionary algorithms: Review and perspective

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SWARM AND evolutionary COMPUTATION 2025年 93卷

作者： Yang, Xu Wang, Rui Li, Kaiwen Ishibuchi, Hisao Natl Univ Def Technol Coll Syst Engn Deya Rd 109 Changsha 410073 Hunan Peoples R China Xiangjiang Lab Changsha 410205 Hunan Peoples R China Southern Univ Sci & Technol Coll Engn Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes. Meta-Black-Box Optimization (MetaBBO) emerges as a pivotal solution, leveraging meta-learning to enhance or discover optimization algorithms automatically. Originating from Automatic Algorithm Design (AAD), MetaBBO has branched into areas such as Learn to Optimize (L2O), Automated Design of Meta-heuristic Algorithm (ADMA), and Automatic evolutionary Computation (AEC), each contributing to the advancement of the field. This comprehensive survey integrates and synthesizes the extant research within MetaBBO for evolutionary algorithms (EAs) to develop a consistent community of this research topic. Specifically, a mathematical model for MetaBBO is established, and its boundaries and scope are clarified. The potential optimization objects in MetaBBO for EAs is explored, providing insights into design space. A taxonomy of MetaBBO methodologies is introduced, reflecting the state-of-the-art from a meta-level perspective. Additionally, a comprehensive overview of benchmarks, evaluation metrics, and platforms is presented, streamlining the research process for those engaged in learning and experimentation in MetaBBO for EA. The survey concludes with an outlook on research, underscoring future directions and the pivotal role of MetaBBO in automatic algorithm design and optimization problem-solving.

关键词： Black-box optimization evolutionary algorithms Automatic algorithm design Learn to optimize Comprehensive survey

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Fixed Parameter Multi-Objective evolutionary algorithms for the W-Separator Problem

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ALGORITHMICA 2025年第4期87卷 537-571页

作者： Baguley, Samuel Friedrich, Tobias Neumann, Aneta Neumann, Frank Pappik, Marcus Zeif, Ziena Potsdam Univ Hasso Plattner Inst Algorithm Engn Prof Dr Helmert Str 2-3 D-14482 Potsdam Brandenburg Germany Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA 5005 Australia

Parameterized analysis provides powerful mechanisms for obtaining fine-grained insights into different types of algorithms. In this work, we combine this field with evolutionary algorithms and provide parameterized complexity analysis of evolutionary multi-objective algorithms for the W-separator problem, which is a natural generalization of the vertex cover problem. The goal is to remove the minimum number of vertices such that each connected component in the resulting graph has at most W vertices. We provide different multi-objective formulations involving two or three objectives that provably lead to fixed-parameter evolutionary algorithms with respect to the value of an optimal solution OPT and W. Of particular interest are kernelizations and the reducible structures used for them. We show that in expectation the algorithms make incremental progress in finding such structures and beyond. The current best known kernelization of the W-separator uses linear programming methods and requires non-trivial post-processing steps to extract the reducible structures. We provide additional structural features to show that evolutionary algorithms with appropriate objectives are also capable of extracting them. Our results show that evolutionary algorithms with different objectives guide the search and admit fixed parameterized runtimes to solve or approximate (even arbitrarily close) the W-separator problem.

关键词： evolutionary algorithms Parameterized complexity Runtime analysis

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Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 142卷

作者： Qiu, Bohan Gao, Kaizhou Yu, Hui Sadollah, Ali Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Macao Peoples R China Univ Sci & Culture Dept Mech Engn Tehran Iran

Distributed assembly permutation flowshop scheduling problem (DAPFSP) have aroused extensive interests in both academia and industry due to its wide range of applications in manufacturing systems. However, the existing literature on this topic is comparatively insufficient. This study investigated the considered problem with total flowtime (TF) criterion by developing three evolutionary algorithms (EAs) and their reinforcement learning (RL) based variants. First, two heuristic rules are proposed to initialize population with diversity. Second, six elaborated local search operators are incorporate with the basic EAs considering the problem's characteristics. Then, the RL algorithms e.g., Q-Learning and Sarsa, are employed to select high-quality local search operators during algorithms' iterations to improve their performance. Three problem characteristic based RL strategies are designed for mapping the RL algorithms and local search operators. Finally, detailed calibration according to 81 large-scale instances is performed to illustrate the effectiveness of the proposed algorithms for solving the DAPFSP.

关键词： evolutionary algorithms Distributed scheduling problems Reinforcement learning Total flowtime

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Generation of algebraic data type values using evolutionary algorithms

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN PROGRAMMING 2025年 143卷

作者： Ballesteros, Ignacio Benac-Earle, Clara Marino, Julio Fredlund, Lars-ake Herranz, Angel IMDEA Software Inst Madrid Spain Univ Politecn Madrid Madrid Spain

Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalize the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques.

关键词： evolutionary algorithms Genetic programming Software testing Property-based testing Red-black tree

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Solving a multiobjective professional timetabling problem using evolutionary algorithms at Mandarine Academy

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第1期32卷 244-269页

作者： Hafsa, Mounir Wattebled, Pamela Jacques, Julie Jourdan, Laetitia Mandarine Acad F-59000 Lille France Univ Lille CNRS Cent Lille UMR 9189CRIStAL F-59000 Lille France

In this paper, we propose a method for solving a real-world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time-consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi-objective evolutionary algorithms (MOEA's) starting with the non-dominated sorting genetic algorithm (NSGA II and NSGA III), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the indicator-based evolutionary algorithm and finally the strength Pareto evolutionary algorithm . Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real-world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution.

关键词： evolutionary algorithms multi-objective optimisation timetabling

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Hybridizing evolutionary algorithms and multiple non-linear regression technique for stream temperature modeling

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ACTA GEOPHYSICA 2025年第3期73卷 2863-2878页

作者： Sedighkia, Mahdi Moradian, Zahra Datta, Bithin Australian Natl Univ Canberra Australia Tarbiat Modares Univ Tehran Iran James Cook Univ Coll Sci & Engn Townsville Australia

The present study hybridizes the new-generation evolutionary algorithms and the nonlinear regression technique for stream temperature modeling and compares this approach with conventional gray and black box approaches under natural flow conditions, providing a comprehensive assessment. The nonlinear equation for water temperature modeling was optimized using biogeography-based optimization (BBO) and invasive weed optimization (IWO), simulated annealing algorithm (SA) and particle swarm optimization (PSO). Two black box approaches, a feedforward neural network (FNN) and a long short-term memory (LSTM) network, were also employed for comparison. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) served as a gray box model for river thermal regimes. The models were evaluated based on accuracy, complexity, generality and interpretability. Performance metrics, such as the Nash-Sutcliffe efficiency (NSE), showed that the LSTM model achieved the highest accuracy (NSE = 0.96) but required significant computational resources. In contrast, evolutionary algorithm-based models offered acceptable performance while reducing the computational complexities of LSTM, with all models achieving NSE values above 0.5. Considering interpretability, accuracy and complexity, evolutionary-based nonlinear models are recommended for general applications, such as assessing thermal river habitats. For tasks requiring very high accuracy, the LSTM model is preferred, while ANFIS provides a balanced trade-off between accuracy and interpretability, making it suitable for engineers and ecologists. While all models demonstrate similar generality, this model is developed for a specific location. For other locations, independent models with a similar architecture would need to be developed. Ultimately, the choice of model depends on specific objectives and available resources.

关键词： Thermal regime River ecosystem Data-driven models evolutionary algorithms Black box models

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Constraint-Feature-Guided evolutionary algorithms for Multi-Objective Multi-Stage Weapon-Target Assignment Problems

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JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 2025年 1-28页

作者： Wang, Danjing Xin, Bin Wang, Yipeng Zhang, Jia Deng, Fang Wang, Xianpeng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China

The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II.

关键词： evolutionary algorithms constrained multi-objective optimization problem constraint handling weapon-target assignment

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