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检索条件"主题词=evolutionary algorithms"

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Using Trainable Segmentation and Watershed Transform for Identifying Unilocular and Multilocular Cysts from Ultrasound Images of Ovarian Tumour

Using Trainable Segmentation and Watershed Transform for Ide...

引用

Conference on Mobile Multimedia/Image Processing, Security, and Applications

作者： Ibrahim, Dheyaa Ahmed Al-Assam, Hisham Du, Hongbo Jassim, Sabah Univ Buckingham Dept Appl Comp Buckingham England

ISBN: (数字)9781510609440

ISBN: (纸本)9781510609433;9781510609440

Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.

关键词： Image segmentation Ultrasonography Image processing algorithms and systems Neural networks Image processing Ovarian cancer Binary data evolutionary algorithms Tumor growth modeling Cancer Computing systems Diagnostics Image analysis Machine learning Medical imaging

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Multi-Objective Optimization of Level of Service in Urban Transportation 17

Multi-Objective Optimization of Level of Service in Urban Tr...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Armas, Rolando Aguirre, Hernan Tanaka, Kiyoshi Shinshu Univ 4-17-1 Wakasato Nagano 3808553 Japan

ISBN: (纸本)9781450349208

This work investigates levels of service in urban transportation coupling a multi-objective evolutionary algorithm with the multi-agent traffic simulator MATSim. The evolutionary algorithm searches combinations of number of private/public transportation users, capacity of buses, and time interval between bus departures minimizing traffic density, travel time and fuel consumption simultaneously. MATSim simulates the movement of 27.000 agents according to the solutions of the evolutionary algorithm on a model of the traffic network of Quito city. We study the trade-off in objectives and analyze the solutions produced to gain knowledge about the conditions to achieve different levels of service. Also, we analyze particulate matter emissions for the trade-off solutions. This work is useful for decision makers to suggest policies that can improve mobility combining private and public transportation.

关键词： Urban transportation level of service multi-objective optimization evolutionary algorithms

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A PSO Fuzzy-Expert System: As an Assistant for Specifying the Acceptance by NOET Measures, at PH.D Level 19

A PSO Fuzzy-Expert System: As an Assistant for Specifying th...

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19th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Mousavi, Seyed Muhammad Hossein MiriNezhad, S. Younes Mosleh, Mehrdad Shafeii Dezfoulian, Mir Hossein Bu Ali Sina Univ Dept Comp Engn Hamadan 65141 Iran

ISBN: (纸本)9781538625859

The intelligent decision making systems are useful tools for the assistance of human expert, and or as a perfect alternative for expert in a variety of auto-decision making fields. The use of such systems in education, agriculture, industry, fishery, animal husbandry etc., can decrease manpower errors or need of it;In the other hand, it can increase the quality and the pace of service giving. The interview at the PH.D level or even Master's degree, due to the high sensitivity in scoring to the candidates, is of high importance. Therefore, creating a system for storing these scores, and inferring the results can be beneficial when there is a large number of candidates. In this paper, the expert system has an educational use, and classifies the probability of acceptance or unacceptance of PH.D candidates in the exam and interview, based on the (National Organization of Educational Testing) NOET measures, also estimates scientific level of candidates. The proposed fuzzy-expert system takes advantage of the particle swarm optimization (PSO) evolutionary algorithm to specifying the score of each variable, and eventually the final condition of the candidate. The acquired results of evaluating the fuzzy-expert system proves its functionality. This system is also able to function well in scoring similar educational cases to specify acceptance.

关键词： PH.D Level NOET Measures Classification Fuzzy-Expert System evolutionary algorithms Particle Swarm Optimization (PSO)

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Multi-Objetive Optimization Problem Mapping Based on Algorithmic Parameter Rankings

Multi-Objetive Optimization Problem Mapping Based on Algorit...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Kakuguchi, Motoaki Miyakawa, Minami Takadama, Keiki Sato, Hiroyuki Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan Hosei Univ 3-7-2 Kajinocho Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538627266

Each of multi-objective optimization problems has its own problem characteristics, and the appropriate evolutionary algorithm and its algorithmic parameters generally depend on each problem. So far optimization problems have been roughly categorized by characteristics of internal functions such as the multimodality, the ill-scaledness, the non-separability. Therektre, it has been difficult to represent line-grained problem similarities among a variety of problems. To visually represent similarities of different optimization problems and appropriate algorithmic parameters for each problem domain having a similar problem characteristic, this work proposes a method to map multi objective optimization problems into a two-dimensional space and visualize appropriate algorithmic parameters all over the two-dimensional problem map. The proposed method uses a set of uniformly distributed algorithmic parameters used in an evolutionary algorithm, ranks them based on their search performance for each problem and use the differences of the parameter rankings among problems as problem similarities. Experimental results using 35 kinds of problems derived from DTLZ models show that the proposed method can map similar problems closely and different problems far in the two-dimensional problem map and visually represent appropriate parameters all over the problem map.

关键词： multi-objective optimization evolutionary algorithms visualization problem mapping parameter mapping

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A novel Harmony Search algorithm embedded with metaheuristic Opposition Based Learning

A novel Harmony Search algorithm embedded with metaheuristic...

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2nd International Symposium on Intelligent Systems Technologies and Applications (ISTA)

作者： Sarkhel, Ritesh Chowdhury, Tithi Mitra Das, Mayuk Das, Nibaran Nasipuri, Mita Jadavpur Univ Dept Comp Sci & Engn Kolkata W Bengal India

evolutionary algorithms (EA) are robust optimization approaches which have been successfully applied to a wide range of problems. However, these well-established metaheuristic strategies are computationally expensive because of their slow convergence rate. Opposition Based Learning (OBL) theory has managed to alleviate this problem to some extent. Through simultaneous consideration of estimates and counter estimates of a candidate solution within a definite search space, better approximation of the candidate solution can be achieved. Although it addresses the slow convergence rate to some extent, it is far from alleviating it completely. The present work proposes a novel approach towards improving the performance of OBL theory by allowing the exploration of a larger search space when computing the candidate solution. Instead of considering all the components of the candidate solution simultaneously, the proposed method considers each of component individually and attempts to find the best possible combination by using a metaheuristic technique. In the present work, this improved Opposition learning theory has been integrated with the classical HS algorithm, to accelerate its convergence rate. A comparative analysis of the proposed method against classical Opposition Based Learning has been performed on a comprehensive set of benchmark functions to prove its superior performance.

关键词： evolutionary algorithms Opposition Based Learning Harmony Search algorithm optimization

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Nonlinguistic Information Extraction by Semi-Supervised Techniques 14

Nonlinguistic Information Extraction by Semi-Supervised Tech...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Semenkina, Maria Akhmedova, Shakhnaz Semenkin, Eugene Siberian State Aerosp Univ Inst Comp Sci & Telecommun Krasnoyarskiy Rabochiy Ave 31 Krasnoyarsk 660014 Russia

ISBN: (纸本)9789897583049

The concept of nonlinguistic information includes all types of extra linguistic information such as factors of age, emotion and physical states, accent and others. Semi-supervised techniques based on using both labelled and unlabelled examples can be an efficient tool for solving nonlinguistic information extraction problems with large amounts of unlabelled data. In this paper a new cooperation of biology related algorithms (COBRA) for semi-supervised support vector machines (SVM) training and a new self-configuring genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural networks (ANN) are presented. Firstly, the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings;and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem.

关键词： Nonlinguistic Information Extraction Semi-supervised Learning Bio-inspired algorithms evolutionary algorithms

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Accelerate SOMA Using Parallel Processing in GPGPU

Accelerate SOMA Using Parallel Processing in GPGPU

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International Conference on Advanced Engineering Theory and Applications (AETA)

作者： Tran Trong Dao Nguyen Mau Toan Vo Hoang Duy Zelinka, Ivan Ton Duc Thang Univ Fac Elect & Elect Engn MERLIN Ho Chi Minh City Vietnam VSB Tech Univ Ostrava Dept Comp Sci 17 Listopadu 15-2172 Ostrava 70833 Czech Republic

ISBN: (纸本)9783319509044;9783319509037

This paper presents methods for implementing SOMA (Self-Organizing Migrating Algorithm) in parallel with the CUDA (Compute Unified Device Architecture) system that can be used to perform the dominant of up-speed when using SOMA algorithm. SOMA has many individual points to find the global minimum which is the key for paralleling this system because each individual can work separately and share the position for all when it moves. Nowadays, due to the humongous size of data and the limitation of the process in single Central Processing Unit (CPU), it becomes impossible to deal with. As a result of these limitations, we need more CPUs working at the same time to do the same job or take advantage of the power of parallel processing in GPGPU (General-Purpose graphics processing unit). Additionally, many supercomputers are built with the need of Parallel Processing in order to meet the power of hardware. Based on the architecture of CUDA, it can handle the threads in SOMA independence. We use two methods with different architecture in CUDA to help SOMA run much faster than single threading method. This paper also uses some techniques to help SOMA work more effective.

关键词： Simulation Optimization evolutionary algorithms Self-organizing migrating algorithms CUDA Parallel processing GPGPU

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OC1-DE: A Differential Evolution Based Approach for Inducing Oblique Decision Trees 16th

OC1-DE: A Differential Evolution Based Approach for Inducing...

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16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Rivera-Lopez, Rafael Canul-Reich, Juana Gamez, Jose A. Puerta, Jose M. Inst Tecnol Veracruz Veracruz Mexico Univ Juarez Autonoma Tabasco Cunduacan Mexico Univ Castilla La Mancha Albacete Spain

ISBN: (纸本)9783319590639;9783319590622

This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy. Considering that: (1) the task of finding an optimal hyperplane with real-valued coefficients is a complex optimization problem in a continuous space, and (2) metaheuristics have been successfully applied for solving this type of problems, in this work a differential evolution algorithm is applied with the aim of finding near-optimal hyperplanes that will be used as test conditions of an oblique decision tree. The experimental results show that this approach induces more accurate classifiers than those produced by other proposed induction methods.

关键词： Machine learning Classification evolutionary algorithms Recursive partition strategy

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Identification of Robust Strain Designs via Tandem pFBA/LMOMA phenotype prediction 17

Identification of Robust Strain Designs via Tandem pFBA/LMOM...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Maia, Paulo Rocha, Isabel Rocha, Miguel SilicoLife Lda Braga Portugal Univ Minho Ctr Biol Engn Braga Portugal

ISBN: (纸本)9781450349390

The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, methods for phenotype prediction were put forward, as well as strain optimization methods, within the field of Metabolic Engineering. evolutionary computation has been on the front line, with the proposal of bilevel metaheuristics, where EC works over phenotype simulation, selecting the most promising solutions for bioengineering tasks. Recently, Schuetz and co-workers proposed that the metabolism of bacteria operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Albeit multi-objective strain optimization approaches focused on bioengineering objectives have been proposed, none tackles the multiobjective nature of the cellular objectives. In this work, we propose multi-objective evolutionary algorithms for strain optimization, where objective functions are defined based on distinct phenotype prediction methods;showing that those can lead to more robust designs, allowing to find solutions in more complex scenarios.

关键词： Systems Biology evolutionary algorithms Metabolic Networks Multiobjective optimization

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Design of Compact Printed Antennas for 5G Base Stations 11

Design of Compact Printed Antennas for <i>5G</i> Base Statio...

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11th European Conference on Antennas and Propagation (EUCAP)

作者： Oliveri, Giacomo Robol, Fabrizio Polo, Alessandro Lombardi, Renato Chuan, Men Mattivi, Maurizio Massagrande, Claudio Vinetti, Pietro Mazzucco, Christian Massa, Andrea Univ Trento ELEDIA Res Ctr ELEDIA UniTN Trento Italy HUAWEI Technol Milan Italy ELEDIA Res Ctr ELEDIA L2S UMR 8506 Gif Sur Yvette France

ISBN: (纸本)9788890701870

An innovative massive multi-objective design procedure is proposed for the synthesis of next-generation antennas for 5G base stations. The 5G antenna design problem is formulated by jointly considering several contrasting requirements in terms of bandwidth, directivity, half-power beamwidth, polarization, and neighbor element isolation. Towards this end, a finite-array model is developed which enables the simulation of a set of adjacent elements during the design process. Thanks to such an approach, the obtained design can be directly included in 5G antenna arrays without further re-optimization to compensate for mutual coupling effects. The resulting massive multi-objective problem is recast as a multi-objective one by suitably clustering the cost function terms according to their physical features, and ad-hoc global search techniques are customized and applied in order to address with the obtained highly non-linear optimization problem. Preliminary numerical results concerning a Paretooptimal tradeoff solution are presented to validate the proposed approach.

关键词： compact antennas printed antennas 5G base station mobile communications multi-objective problem evolutionary algorithms

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