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EvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC

作者： Tsunoda, Denise F. Lopes, Heitor S. Freitas, Alex A. Lab. Bioinformática Centro Federal Educ. Tecnol. do Paraná Curitiba Brazil Computing Laboratory University of Kent Canterbury United Kingdom

Proteins can be grouped into families according to their biological functions. This paper presents a system, named GAMBIT, which discovers motifs (particular sequences of amino acids) that occur very often in proteins of a given family but rarely occur in proteins of other families. These motifs are used to classify unknown proteins, that is, to predict their function by analyzing the primary structure. To search for motifs in proteins, we developed a GA with specially tailored operators for the problem. GAMBIT was compared with MEME, a web tool for finding motifs in the TransMembrane Protein DataBase. Motifs found by both methods were used to build a decision tree and classification rules, using, respectively, C4.5 and Prism algorithms. Motifs found by GAMBIT led to significantly better results, when compared with those found by MEME, using both classification algorithms. © Springer-Verlag Berlin Heidelberg 2005.

关键词： evolutionary algorithms

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evolutionary search based fuzzy self-organizing clustering

Evolutionary search based fuzzy self-organizing clustering

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1999 Congress on evolutionary Computation, CEC 1999

作者： Yao, Susu Tongyuan University No 2 Biao Ying Nanjing 210016 China

This paper presents a hybrid method of fuzzy clustering using evolutionary search and self-organization, which has the advantages of both. Simulation experiments have shown that the convergence speed of the proposed m... 详细信息

ISBN: (纸本)0780355369

关键词： evolutionary algorithms

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Optimized white matter fiber reconstruction with b-spline curve and evolutionary computation

Optimized white matter fiber reconstruction with b-spline cu...

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5th International Conference on Natural Computation, ICNC 2009

作者： Wu, Xi Bi, Wuzhong Zhu, Jingyu Yang, Ling Xie, Mingyuan Department of Electronic Engineering Chengdu University of Information Technology 610225 Chengdu Sichuan China

ISBN: (纸本)9780769537368

Diffusion tensor tractography can reconstruct white matter fiber bundle non-invasively. In this paper, a novel fiber tracking algorithm is proposed based on an adaption of evolutionary computation. Given a pair of ROIs, the proposed algorithm can generate globally optimized fiber pathways in terms of fiber geometric smoothness and consistency with the tensor field with the B-spline curve model. The experiments demonstrate that the fiber tracking algorithm we proposed can reconstruct white matter fiber trajectories faithfully for both synthetic and in vivo DTI data. Compared to existing methods, the new tracking algorithm incorporates a global constraint, thus making it insusceptible to image noise and other local artifacts. © 2009 IEEE.

关键词： evolutionary algorithms

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Optimal disassembly scheduling using an evolutionary algorithm based on a tree structure

Optimal disassembly scheduling using an evolutionary algorit...

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2018 International Symposium on Flexible Automation, ISFA 2018

作者： Tanaka, Yousuke Tanimizu, Yoshitaka Graduate school of engineering Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-shi Osaka599-8531 Japan

In recent years, it is required to reduce the amount of waste since there is a growing interest in environmental issues. Hence, it is important to reuse and recycle the used products efficiently. In this paper, we propose a scheduling method by using an evolutionary algorithm in order to find an optimal disassembly schedule including a lot of components of the used products. The relationship between two components in assembly structure can be represented by a tree structure. A whole disassembly process of a product is represented by a combination of tree structures. Each tree structure is exchanged by genetic operations such as crossover and mutation. We carry out a lot of computational experiments though the developed scheduling system. The experimental results demonstrate that the new method is superior to the previous method from the viewpoint of the minimization of the whole disassembly processing times. Copyright © 2018 by ISFA.

关键词： evolutionary algorithms

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A Multiobjective Multitask evolutionary Algorithm Based on Decomposition and Multivariate Gaussian Distribution 7

A Multiobjective Multitask Evolutionary Algorithm Based on D...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wu, Zhongjian Lin, Wu Tang, Huimei Lin, Qiuzhen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9781665441490

evolutionary multiobjective multitask optimization (EMTO) has attracted widespread attention in recent years, which solves multiple tasks simultaneously in a single population. How to extract effective knowledge and recognize valuable transferred solutions is the key to enhance the performance of EMTO. However, few research studies consider these two issues at the same time. To fill this research gap, we propose a novel multiobjective multitask evolutionary algorithm based on decomposition and multivariate Gaussian distribution, called MTEA-DMG, in which a candidate transferred solution set is generated using resource allocation according to the priority of subproblems to multiple tasks. Then, one most valuable knowledge transfer carrier is selected by online statistical estimation of distribution density. The experimental results on a set of benchmark problems with different degrees of similarity show that MTEA-DMG is superior to other state-of-the-art EMTO algorithms. © 2021 IEEE.

关键词： evolutionary algorithms

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Solving Order Batching/Picking Problems with an evolutionary Algorithm 10th

Solving Order Batching/Picking Problems with an Evolutionary...

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10th International Conference of Production Research - Americas, ICPR-Americas 2020

作者： Miguel, Fabio M. Frutos, Mariano Méndez, Máximo Tohmé, Fernando Universidad Nacional de Río Negro Sede Alto Valle y Valle Medio Villa Regina Argentina Departamento de Ingeniería Universidad Nacional del Sur e IIESS UNS-CONICET Bahía Blanca Argentina Instituto Universitario de Sistemas Inteligentes SIANI Universidad de Las Palmas de Gran Canaria ULPGC Las Palmas Spain Departamento de Economía Universidad Nacional del Sur e INMABB UNS-CONICET Bahía Blanca Argentina

ISBN: (纸本)9783030763060

We present an evolutionary algorithm to solve a combination of the Order Batching and Order Picking problems. This integrated problem consists of selecting and picking up batches of various items requested by customers from a storage area, given a deadline for finishing each order according to a delivery plan. We seek to find the plan that minimizes the total cost of picking the goods, proportional to the time devoted to traverse the storage facility, grabbing the good and leaving it at the dispatch area. Earliness and tardiness induce inefficiency costs due to the excess use of space or breaching the delivery contracts. The results of running the algorithm compare favorably to those reported in the literature. © 2021, Springer Nature Switzerland AG.

关键词： evolutionary algorithms

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An evolutionary algorithm for single objective nonlinear constrained optimization problems

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AISB International Workshop on evolutionary Computing, 1997

作者： Albright, Hunter T. Ignizio, James P. Department of Systems Engineering University of Virginia CharlottesvilleVA United States

ISBN: (纸本)3540634762

This paper presents research into an evolutionary, algorithm that utilizes the co evolution of feasible and infeasible solutions in solving constrained optimization problems. The evolution of these populations occurs through the use of traditional and specially designed operators that allow for crossover to occur in each of the populations as well as across the two populations. The cross population crossover allows for the information contained in the infeasible solutions to be utilized in the search for the optimal solution. © Springer-Verlag Berlin Heidelberg 1997.

关键词： evolutionary algorithms

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Solving Shubert function optimization problem by using thermodynamics evolutionary algorithm

Solving Shubert function optimization problem by using therm...

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2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010

作者： Wang, Xuan Wang, Shao-Song Xiao, Li Department of Information Technology HuaZhong Normal University Wuhan China

ISBN: (纸本)9781424453153

Based on the review of recent development of evolutionary computation and the principle of free energy minimization of thermodynamics, a new thermodynamics evolutionary algorithm (TDEA) for solving Shubert function optimization problem is proposed. Numerical experiments are conducted to measure the performance of thermodynamics evolutionary algorithm. The results show that thermodynamics evolutionary algorithm is of potential to obtain global optimum or more accurate solutions than other evolutionary methods. ©2010 IEEE.

关键词： evolutionary algorithms

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A novel MPPT scheme for solar powered boost inverter using evolutionary programming

A novel MPPT scheme for solar powered boost inverter using e...

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2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering, IConRAEeCE'11

作者： Kaliamoorthy, M. Rajasekaran, V. Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9781457721472

Due to its ability to handle nonlinear functions regardless of the derivative information, evolutionary Programming (EP) are envisaged to be very effective for Maximum Power Point Tracking (MPPT) of Photovoltaic cell (PV). This paper proposes a critical evaluation of MPPT algorithm of PV model using EP. The proposed model is then compared with the conventional Newton raphson algorithm. The simulation results obtained from the EP were found to be more accurate with the real maximum power point. The algorithm has better accuracy especially at low irradiance level that allows for a more accurate prediction of PV system performance. The EP based MPPT algorithm was tested with single stage boost inverter. © 2011 IEEE.

关键词： evolutionary algorithms

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Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization

arXiv

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arXiv 2025年

作者： Guo, Hongshu Qiu, Wenjie Ma, Zeyuan Zhang, Xinglin Zhang, Jun Gong, Yue-Jiao South China University of Technology China Nankai University China Hanyang University Korea Republic of

Recent research in Cooperative Coevolution (CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary limitation in that they require deep expertise for selecting or designing effective variable decomposition strategies. Inspired by advancements in Meta-Black-Box Optimization, this paper introduces LCC, a pioneering learning-based cooperative coevolution framework that dynamically schedules decomposition strategies during optimization processes. The decomposition strategy selector is parameterized through a neural network, which processes a meticulously crafted set of optimization status features to determine the optimal strategy for each optimization step. The network is trained via the Proximal Policy Optimization method in a reinforcement learning manner across a collection of representative problems, aiming to maximize the expected optimization performance. Extensive experimental results demonstrate that LCC not only offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption, but it also exhibits promising transferability towards unseen problems. © 2025, CC BY.

关键词： evolutionary algorithms

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