Relevant bibliographies by topics / Cuckoo Search and Genetic algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cuckoo Search and Genetic algorithm'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Cuckoo Search and Genetic algorithm"

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Li, Juan, Dan-dan Xiao, Hong Lei, Ting Zhang, and Tian Tian. "Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location." Mathematics 8, no. 2 (2020): 149. http://dx.doi.org/10.3390/math8020149.

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculatin
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Liao, Qixiang, Shudao Zhou, Hanqing Shi, and Weilai Shi. "Parameter Estimation of Nonlinear Systems by Dynamic Cuckoo Search." Neural Computation 29, no. 4 (2017): 1103–23. http://dx.doi.org/10.1162/neco_a_00946.

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In order to address with the problem of the traditional or improved cuckoo search (CS) algorithm, we propose a dynamic adaptive cuckoo search with crossover operator (DACS-CO) algorithm. Normally, the parameters of the CS algorithm are kept constant or adapted by empirical equation that may result in decreasing the efficiency of the algorithm. In order to solve the problem, a feedback control scheme of algorithm parameters is adopted in cuckoo search; Rechenberg’s 1/5 criterion, combined with a learning strategy, is used to evaluate the evolution process. In addition, there are no information
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Banu, P. K. Nizar, and S. Andrews. "Gene Clustering Using Metaheuristic Optimization Algorithms." International Journal of Applied Metaheuristic Computing 6, no. 4 (2015): 14–38. http://dx.doi.org/10.4018/ijamc.2015100102.

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Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuckoo Search is one such optimization algorithm inspired from nature by breeding strategy of parasitic bird, the cuckoo. This paper proposes cuckoo search clustering and clustering using levy flight cuckoo
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Chao, Chun-Tang, Ming-Tang Liu, Chi-Jo Wang, and Juing-Shian Chiou. "A fuzzy adaptive controller for cuckoo search algorithm in active suspension system." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 3 (2018): 761–71. http://dx.doi.org/10.1177/1461348418811473.

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This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the
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TEOH, CHONG KEAT, ANTONI WIBOWO, and MOHD SALIHIN NGADIMAN. "AN ADAPTED CUCKOO OPTIMIZATION ALGORITHM AND GENETIC ALGORITHM APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM." International Journal of Computational Intelligence and Applications 13, no. 01 (2014): 1450002. http://dx.doi.org/10.1142/s1469026814500023.

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The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Lévy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algor
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Yin, Lu, Junlin Qiu, and Shangbing Gao. "Biclustering of Gene Expression Data Using Cuckoo Search and Genetic Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (2018): 1850039. http://dx.doi.org/10.1142/s0218001418500398.

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Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local sea
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Reddy T, Chandrasekhara, Srivani V, A. Mallikarjuna Reddy, and G. Vishnu Murthy. "Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm." International Journal of Engineering & Technology 7, no. 4.6 (2018): 275. http://dx.doi.org/10.14419/ijet.v7i4.6.20489.

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For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our pro
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Ojha, Muktesh Kumar, Amrita Rai, Anshuman Prakash, Priyesh Tiwari, and Dhiraj Gupta. "Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement." Traitement du Signal 39, no. 4 (2022): 1387–97. http://dx.doi.org/10.18280/ts.390433.

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Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The
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Chen, Dechao, Zhixiong Wang, Guanchen Zhou, and Shuai Li. "Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance." Sustainability 14, no. 22 (2022): 15137. http://dx.doi.org/10.3390/su142215137.

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In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and
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Virk, Amandeep Kaur, and Kawaljeet Singh. "Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms." Journal of Industrial Integration and Management 03, no. 02 (2018): 1850009. http://dx.doi.org/10.1142/s2424862218500094.

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This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search technique
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Dissertations / Theses on the topic "Cuckoo Search and Genetic algorithm"

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Lidberg, Simon. "Evolving Cuckoo Search : From single-objective to multi-objective." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-5309.

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This thesis aims to produce a novel multi-objective algorithm that is based on Cuckoo Search by Dr. Xin-She Yang. Cuckoo Search is a promising nature-inspired meta-heuristic optimization algorithm, which currently is only able to solve single-objective optimization problems. After an introduction, a number of theoretical points are presented as a basis for the decision of which algorithms to hybridize Cuckoo Search with. These are then reviewed in detail and verified against current benchmark algorithms to evaluate their efficiency. To test the proposed algorithm in a new setting, a real-world
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Lakshminarayanan, Srinivasan. "Nature Inspired Discrete Integer Cuckoo Search Algorithm for Optimal Planned Generator Maintenance Scheduling." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438101954.

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Lin, Yu-Min. "Tabu Search and Genetic Algorithm for Phylogeny Inference." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-10092008-235130/.

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Phylogenetics is the study of evolutionary relations between different organisms. Phylogenetic trees are the representations of these relations. Researchers have been working on finding fast and systematic approaches to reconstruct phylogenetic trees from observed data for over 40 years. It has been shown that, given a certain criterion to evaluate each tree, finding the best fitted phylogenetic trees among all possible trees is an NP-hard problem. In this study, we focus on the topology searching techniques for the maximum-parsimony and maximum-likelihood phylogeny inference. We proposed two
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Al-Dallal, Ammar Sami. "Enhancing recall and precision of web search using genetic algorithm." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7379.

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Due to rapid growth of the number of Web pages, web users encounter two main problems, namely: many of the retrieved documents are not related to the user query which is called low precision, and many of relevant documents have not been retrieved yet which is called low recall. Information Retrieval (IR) is an essential and useful technique for Web search; thus, different approaches and techniques are developed. Because of its parallel mechanism with high-dimensional space, Genetic Algorithm (GA) has been adopted to solve many of optimization problems where IR is one of them. This thesis propo
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Zhang, Xiaoyu. "Effective Search in Online Knowledge Communities: A Genetic Algorithm Approach." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35059.

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Online Knowledge Communities, also known as online forum, are popular web-based tools that allow members to seek and share knowledge. Documents to answer varieties of questions are associated with the process of knowledge exchange. The social network of members in an Online Knowledge Community is an important factor to improve search precision. However, prior ranking functions donâ t handle this kind of document with using this information. In this study, we try to resolve the problem of finding authoritative documents for a user query within an Online Knowledge Community. Unlike prior rankin
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Kong, Garry. "An Investigation of Complex Visual Search With The Genetic Algorithm." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16326.

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Visual search is an everyday task, defined as the search for a target amongst distractors. Visual search has historically been thought to reflect primarily early perceptual processes, with little influence of cognitive processes. However, recent research suggests that this is an artefact of the simple stimuli typically used in the field. In this thesis, I will use the genetic algorithm to study visual search for complex stimuli. In part 1, I use the genetic algorithm to investigate a display comprised of 119 distractors, each with an orientation, colour and size. Results suggest that distracto
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Yamashiro, Daisuke, Tomohiro Yoshikawa, and Takeshi Furuhashi. "Visualization of Search Process and Improvement of Search Performance in Multi-Objective Genetic Algorithm." IEEE, 2006. http://hdl.handle.net/2237/9486.

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Bilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.

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Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectories
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Nguyen, Quoc Tuan. "Using the genetic algorithm to optimize Web search: Lessons from biology." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27160.

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Searching for information on the Web is a relatively inefficient process. My goal is to develop a method that optimizes web search queries without user intervention. Developing intelligent ways to automate this process includes the development of algorithms that automatically manipulate the use of keywords to produce the desired output. Genetic algorithms (GA) provide a potentially useful approach in this area. However, these approaches have not fully exploited the biological concepts associated with genetic reproduction and evolution. I hypothesize that an approach that uses GA but modifies
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Harris, Richard Alan. "An analysis of the genetic algorithm and abstract search space visualisation." Thesis, University of Plymouth, 1996. http://hdl.handle.net/10026.1/2087.

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The Genetic Algorithm (Holland, 1975) is a powerful search technique based upon the principles of Darwinian evolution. In its simplest form the GA consists of three main operators - crossover, mutation and selection. The principal theoretical treatment of the Genetic Algorithm (GA) is provided by the Schema Theorem and building block hypothesis (Holland, 1975). The building block hypothesis describes the GA search process as the combination, sampling and recombination of fragments of solutions known as building blocks. The crossover operator is responsible for the combination of building block
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Books on the topic "Cuckoo Search and Genetic algorithm"

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Yang, Xin-She, ed. Cuckoo Search and Firefly Algorithm. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02141-6.

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Dey, Nilanjan, ed. Applications of Cuckoo Search Algorithm and its Variants. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5163-5.

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Yang, Xin-She. Cuckoo Search and Firefly Algorithm: Theory and Applications. Springer International Publishing AG, 2016.

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Yang, Xin-She. Cuckoo Search and Firefly Algorithm: Theory and Applications. Springer, 2013.

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Dey, Nilanjan. Applications of Cuckoo Search Algorithm and Its Variants. Springer Singapore Pte. Limited, 2021.

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Dey, Nilanjan. Applications of Cuckoo Search Algorithm and its Variants. Springer, 2020.

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Dey, Nilanjan. Applications of Cuckoo Search Algorithm and its Variants. Springer, 2020.

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Yang, Xin-She. Cuckoo Search and Firefly Algorithm: Theory and Applications. Springer London, Limited, 2013.

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Vose, Michael D. The Simple Genetic Algorithm. The MIT Press, 1999. http://dx.doi.org/10.7551/mitpress/6229.001.0001.

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The Simple Genetic Algorithm (SGA) is a classical form of genetic search. Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (i.e., proven) about the theory of the SGA. He also makes available algorithms for the computation of mathematical objects related to the SGA. Although he describes the SGA in terms of heuristic search, the book is not about search or optimization per se. Rather, the focus is on the SGA as an evolutionary system. The author intends the book also to serve as an outline for exploring topics in mathematics and computer scienc
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Genetic Relational Search for Inductive Concept Learning: A Memetic Algorithm for ILP. LAP Lambert Academic Publishing, 2010.

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Book chapters on the topic "Cuckoo Search and Genetic algorithm"

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Yun, Youngsu, Jungbok Jo, and Mitsuo Gen. "Adaptive Hybrid Genetic Algorithm with Modified Cuckoo Search for Reliability Optimization Problem." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1837-4_31.

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Majima, Takahiro, Keiki Takadma, Daisuke Watanabe, and Mitujiro Katuhara. "Generating Hub-Spoke Network for Public Transportation: Comparison Between Genetic Algorithm and Cuckoo Search Algorithm." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49049-6_19.

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Khan, Rijwan, Mohd Amjad, and Akhlesh Kumar Srivastava. "Optimization of Automatic Test Case Generation with Cuckoo Search and Genetic Algorithm Approaches." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3773-3_40.

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Guerrero, Maribel, Oscar Castillo, and Mario García. "Cuckoo Search via Lévy Flights and a Comparison with Genetic Algorithms." In Studies in Computational Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10960-2_6.

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Ghosh, Tarun Kumar, Sanjoy Das, Subhabrata Barman, and Rajmohan Goswami. "A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2525-9_14.

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Shehab, Mohammad. "Cuckoo Search Algorithm." In Artificial Intelligence in Diffusion MRI. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36083-2_3.

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Kaveh, Ali, and Taha Bakhshpoori. "Cuckoo Search Algorithm." In Metaheuristics: Outlines, MATLAB Codes and Examples. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04067-3_7.

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Yang, Xin-She, and Adam Slowik. "Cuckoo Search Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-9.

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Zolghadr-Asli, Babak. "Cuckoo Search Algorithm." In Computational Intelligence-based Optimization Algorithms. CRC Press, 2023. http://dx.doi.org/10.1201/9781003424765-13.

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Cuevas, Erik, and Alma Rodríguez. "Cuckoo Search (CS) Algorithm." In Metaheuristic Computation with MATLAB®. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-8.

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Conference papers on the topic "Cuckoo Search and Genetic algorithm"

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Bargali, Anushka, and Sandeep Kumar Sunori. "Optimization of Biosensor Parameters using Cuckoo Search Algorithm." In 2025 7th International Conference on Inventive Material Science and Applications (ICIMA). IEEE, 2025. https://doi.org/10.1109/icima64861.2025.11073996.

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Jain, Shilpa, Sandeep Kumar Sunori, Amit Mittal, and Pradeep Juneja. "Cuckoo Search Algorithm Based Intrusion Detection System of Cybersecurity." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883367.

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Peng, Hu, Changshou Deng, Hui Wang, Wenjun Wang, Xinyu Zhou, and Zhijian Wu. "Gaussian bare-bones cuckoo search algorithm." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3205666.

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Kumar, Anil, and Shampa Chakarverty. "Design optimization using Genetic Algorithm and Cuckoo Search." In 2011 IEEE International Conference on Electro/Information Technology (EIT 2011). IEEE, 2011. http://dx.doi.org/10.1109/eit.2011.5978616.

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Wang, Junrui, Xiang Shang, Ten Guo, Jinchao Zhou, Sining Jia, and Chuang Wang. "Optimal Path Planning Based on Hybrid Genetic-Cuckoo Search Algorithm." In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010519.

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Navarro, M. M., and B. B. Navarro. "Hybrid Cuckoo Search and Genetic Algorithm for Optimizing Electricity Forecast." In 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2023. http://dx.doi.org/10.1109/ieem58616.2023.10406830.

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Nooraliei, A., M. R. Meybodi, and B. Masoumi. "Memetic algorithm based on genetic algorithm and improved cuckoo search algorithm for dynamic environment." In 2016 Artificial Intelligence and Robotics (IRANOPEN). IEEE, 2016. http://dx.doi.org/10.1109/rios.2016.7529490.

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KAYA, Yasin. "Comparison of Using the Genetic Algorithm and Cuckoo Search for Feature Selection." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620823.

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Wira Hadipoernomo, Jessica Christa, and Bryan C. Watson. "Sandpiper Food Search Algorithm: A New Optimization Algorithm With Limited Knowledge." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142709.

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Abstract Optimization problems in mechanical engineering play a crucial role in refining complex system designs, improving performance, and maximizing efficiency across various applications. These problems have become increasingly complex, stretching the limits of conventional methods such as convex optimization or the Newton-Raphson method. To address this challenge, numerous metaheuristic algorithms, often inspired by nature, have emerged including Particle Swarm Optimization, Genetic Algorithm, Bat Algorithm, Firefly Algorithm, and Cuckoo Search. However, most of these existing algorithms h
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Alkhateeb, Faisal, and Ra'ed M. Al-Khatib. "A hybrid general framework of cuckoo, immune and genetic search algorithms." In ICEMIS'19: The 5th International Conference on Engineering & MIS 2019. ACM, 2019. http://dx.doi.org/10.1145/3330431.3330432.

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Reports on the topic "Cuckoo Search and Genetic algorithm"

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Van Zandt, James R. A Genetic Algorithm for Search Route Planning. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada254894.

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optim
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