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A bio-inspired optimization algorithm with disjoint sets to delineate orthogonal site-specific management zones

PRECISION AGRICULTURE 2025年第1期26卷 1-34页

作者： Vicencio-Medina, Salvador J. Rios-Solis, Yasmin A. Cid-Garcia, Nestor M. Tecnol Monterrey Sch Engn & Sci Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico CONAHCYT Ctr Invest Ciencias Informac Geoespacial Lab Nacl Geointeligencia Circuito Tecnopolo Norte 107 Aguascalientes 20313 Ags Mexico

The first stage in the precision agriculture cycle has been a vital study area in recent years because it allows soil testing followed by data analysis. In this stage, a strategic delineation of site-specific management zones acquires a particular interest because it enables site-specific treatment to improve crop yield by efficiently using the input of resources. The delineation of site-specific management zones problem is to determine the minimum number of zones that cover the entire field so that each zone's homogeneity is significant according to a specific biological, chemical, or physical soil property. Furthermore, the delineated zones should be orthogonal-shaped to be practical for agricultural machinery. This work has proposed a new bio-inspired algorithm, specifically an estimation of distribution algorithm, based on a decoder that heavily relies on the Disjoint-Set algorithm and a new reactive penalized fitness function that detects unfeasible solutions. The new methodology improves the solutions presented in the literature by using a new search engine that drastically reduces the computational times of similar algorithms. Our algorithm has been tested with the literature benchmark, considering a new reactive penalization in the fitness function. It obtains the best solutions for 66.66% of the instances benchmark compared to the best literature method. Due to the algorithm's efficiency, a new set of larger instances is introduced to test the scalability and robustness of the method. It obtained an efficiency of 79.3%.

关键词： Site-specific management zones Precision agriculture Orthogonal-shape zones estimation of distribution algorithm Disjoint set union

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Quantum Parametric Circuit Optimization with estimation of distribution algorithms 22

Quantum Parametric Circuit Optimization with Estimation of D...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Soloviev, Vicente P. Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781450392686

Variational quantum algorithms (VQAs) offer some promising characteristics for carrying out optimization tasks in noisy intermediate-scale quantum devices. These algorithms aim to minimize a cost function by optimizing the parameters of a quantum parametric circuit. Thus, the overall performance of these algorithms, heavily depends on the classical optimizer which sets the parameters. In the last years, some gradient-based and gradient-free approaches have been applied to optimize the parameters of the quantum circuit. In this work, we follow the second approach and propose the use of estimation of distribution algorithms for the parameter optimization in a specific case of VQAs, the quantum approximate optimization algorithm. Our results show an statistically significant improvement of the cost function minimization compared to traditional optimizers.

关键词： Quantum optimization variational quantum algorithm estimation of distribution algorithm max cut

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Solving TSP Problems with Hybrid estimation of distribution algorithms

Solving TSP Problems with Hybrid Estimation of Distribution ...

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10th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Xiaoxia Ma, Yunyong Univ Sci & Technol Liaoning Coll Software Engn Anshan Peoples R China

ISBN: (纸本)9783319093338;9783319093321

In this paper, a hybrid estimation of distribution algorithms is proposed to solve traveling salesman problem, and a greedy algorithm is used to improve the quality of the initial population. It sets up a Bayes probabilistic model of the TSP. The roulette method is adopted to generate the new population. In order to prevent falling into local optimum, the mutation and limit were proposed to enhance the exploitation ability. At the same time, three new neighborhood search strategies and the second element optimization method are presented to enhance the ability of the local search. The simulation results and comparisons based on benchmarks validate the efficiency of the proposed algorithm.

关键词： estimation of distribution algorithm Traveling salesman problem Neighborhood search

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Prototype selection and feature subset selection by estimation of distribution algorithms.: A case study in the survival of cirrhotic patients treated with TIPS 1

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8th Conference on Artificial Intelligence in Medicine in Europe (AIME 2001)

作者： Sierra, B Lazkano, E Inza, I Merino, M Larrañaga, P Quiroga, J Univ Basque Country Dept Comp Sci & Artificial Intelligence E-20080 Donostia Spain Basque Hlth Serv Osakidetza E-20013 Donostia San Sebastian Spain Univ Clin Navarra Fac Med E-31080 Pamplona Iruna Spain

ISBN: (数字)9783540482291

ISBN: (纸本)3540422943

The Transjugular Intrahepatic Portosystemic Shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staffs experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first six months after TIPS placement. An investigation for predicting the conduct of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. We have applied a new estimation of distribution algorithms based approach in order to perform a Prototype and Feature Subset Selection to improve the classification accuracy obtained using all the variables and all the cases. Used paradigms are K-Nearest Neighbours, Artificial Neural Networks and Classification Trees.

关键词： machine learning prototype selection feature subset selection transjugular intrahepatic Portosystemic shunt estimation of distribution algorithm indications

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Discovering Fuzzy-Rough Reducts through estimation of distribution algorithms

Discovering Fuzzy-Rough Reducts through Estimation of Distri...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jensen, Richard Mac Parthalain, Neil Aberystwyth Univ Inst Maths Phys & Comp Sci IMPACS Dept Comp Sci Aberystwyth SY23 3FG Dyfed Wales

ISBN: (纸本)9781467374286

Due to the explosive growth of stored information worldwide, feature selection (FS) is becoming an increasingly important step, particularly given the abundance of noisy, irrelevant or misleading features. The main aim of FS is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original set of features. However, the problem of finding optimal reductions is challenging as there is always a trade-off between the extent of reduction and the resulting information loss. This topic has been of particular interest in rough and fuzzy-rough set theory, as these provide a mechanism for defining optimality using only the data itself. Evolutionary methods have been used to try to find rough and fuzzy-rough optimal reductions, but these approaches ignore the fact that not all equally-sized reducts have the same utility for classifiers. This paper presents a novel approach for fuzzy-rough feature selection that uses estimation of distribution algorithms to maintain information about the quality of features, to then obtain a better quality reduct that is more useful in general.

关键词： distributed algorithms feature selection fuzzy set theory rough set theory EDA estimation of distribution algorithm fuzzy-rough feature selection fuzzy-rough reducts quality reduct subset Approximation methods estimation Genetic algorithms Noise measurement Set theory Sociology Statistics Allocation algorithm Set Theory Feature extraction Noise measurement Approximation method Sociology Rough set theory distributed algorithms Fuzzy Set Theory Genetic algorithm (GA) Subset European Defence Agency electronic design automation Electronic design automation and methodology EDA gene

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Improving estimation of distribution algorithms with Heavy-Tailed Student's t distributions 10

Improving Estimation of Distribution Algorithms with Heavy-T...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Liu, Bin Cheng, Shi Shi, Yuhui Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Jiangsu Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781538643624

As a derivative-free optimization method, the estimation of distribution algorithm (EDA) usually leverages a Gaussian or a Gaussian mixture model to represent the distribution of promising solutions that have been found so far. This paper investigates the application of an alternative model, namely the heavier-tailed Student's t distribution, to implement EDA. Two corresponding algorithms, termed ESTDA and EMSTDA, are developed, respectively. The ESTDA employs a single Student's t model to represent the distribution of the promising solutions. The EMSTDA uses a mixture of Student's t models to take account of hard multimodal cases. These methods are evaluated through extensive and in-depth numerical experiments using over a dozen of benchmark objective functions. Empirical results show that they provide remarkably better performance than their Gaussian counterparts in most cases under consideration.

关键词： estimation of distribution algorithm EDA derivative-free optimization Student's t distribution mixture

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Application of estimation of distribution algorithms for Solving Order Acceptance with Weighted Tardiness Problems

Application of Estimation of Distribution Algorithms for Sol...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Wattanapornprom, Watcharee Li, Tieke Wattanapornprom, Warin Chongstitvatana, Prabhas Univ Sci & Technol Beijing Dongling Sch Econ & Management Beijing 100083 Peoples R China Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781479909865

Over the past decade the strategic importance of order acceptance has been widely recognized in practice. This research studies the adaptation of estimation of distribution algorithm to solve the order acceptance decisions when capacity is limited. The results show that the four new estimation of distribution algorithms are all better than the genetic algorithm with local search.

关键词： Order acceptance combinatorial optimization estimation of distribution algorithm genetic algorithm

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Effective and Efficient estimation of distribution algorithms for Permutation and Scheduling Problems

Effective and Efficient Estimation of Distribution Algorithm...

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作者： Mayowa Ayodele Robert Gordon University

学位级别：博士

estimation of distribution algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Proba- bilistic models are used to represent relationships between solution vari- ables which may give useful, human-understandable insights into real- world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as per- mutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for per- mutation representation. EDAs can now produce state-of-the-art perfor- mance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redun- dancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improve- ment procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reduc- ing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive re- sults on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements.

关键词： estimation of distribution algorithm Probabilistic Model Random Key Genetic algorithm Real-world Project Scheduling Problem Optimisation Gaussian distribution Permutation Flowshop Scheduling Problem

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Optimization of Machining Parameters Using estimation of distribution algorithms

Optimization of Machining Parameters Using Estimation of Dis...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Xie, Shu-tong Guo, Yin-biao Huang, Hai-bin Lin, Jing Xiamen Univ Dept Mech & Elect Engn Xiamen 361005 Peoples R China

ISBN: (纸本)9781424447541

In computer numerical control (CNC) machining problems, it is important to reduce the production cost To deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in multi-pass turning operations, two estimation of distribution algorithms (EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining parameters Computer simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters, which significantly reduce the unit production cost

关键词： Machining parameters Optimization estimation of distribution algorithm Turning

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Optimization of Machining Parameters for Parallel Turnings Using estimation of distribution algorithms

Optimization of Machining Parameters for Parallel Turnings U...

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3rd International Conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Xie, Shutong Pan, Lifang Jimei Univ Sch Comp Engn Xiamen 361021 Peoples R China Jimei Univ Sch Sci Xiamen 361021 Peoples R China

ISBN: (纸本)9783037857649

Optimal machining parameters can lead to considerable savings in manufacturing problems. In this paper, to deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in parallel turnings, we propose a novel optimization approach which divides this complicated problem into several sub-problems. Then an estimation of distribution algorithm (EDA) is developed to search the optimal results for each sub-problem. Computer simulations show that the proposed approach is efficient in searching the optimal solutions to reduce significantly the unit production cost.

关键词： Parallel turning Cutting parameters Optimization estimation of distribution algorithm

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