Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
Автор: Keller Название: Fundamentals of Computational Intelligence - Neural Networks, Fuzzy Systems, and Evolutionary Computation ISBN: 1119214343 ISBN-13(EAN): 9781119214342 Издательство: Wiley Рейтинг: Цена: 104550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation.
Автор: Takeshi Furuhashi; Yoshiki Uchikawa Название: Fuzzy Logic, Neural Networks, and Evolutionary Computation ISBN: 3540619887 ISBN-13(EAN): 9783540619888 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume contains a selection of 12 revised papers chosen from the 4th IEEE/Nagoya University World Wisepersons Workshop held in Nagoya, Japan, November 14-15, 1995. The papers presented are organized into sections including fuzzy and evolutionary computation, and fuzzy and learning automata.
Автор: Amir H Gandomi Название: Evolutionary Computation in Scheduling ISBN: 111957384X ISBN-13(EAN): 9781119573845 Издательство: Wiley Рейтинг: Цена: 109770.00 T Наличие на складе: Поставка под заказ. Описание:
Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems
This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches.
Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book:
Provides a representative sampling of real-world problems currently being tackled by practitioners
Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence
Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems
Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
Название: Evolutionary approach to machine learning and deep neural networks. ISBN: 9811301999 ISBN-13(EAN): 9789811301995 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Hitoshi Iba Название: Evolutionary Approach to Machine Learning and Deep Neural Networks ISBN: 9811343586 ISBN-13(EAN): 9789811343582 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Paul Kaufmann; Pedro A. Castillo Название: Applications of Evolutionary Computation ISBN: 3030166910 ISBN-13(EAN): 9783030166915 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 22nd International Conference on Applications of Evolutionary Computation, EvoApplications 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoMUSART.The 44 revised full papers presented were carefully reviewed and selected from 66 submissions. They were organized in topical sections named: Engineering and Real World Applications; Games; General; Image and Signal Processing; Life Sciences; Networks and Distributed Systems; Neuroevolution and Data Analytics; Numerical Optimization: Theory, Benchmarks, and Applications; Robotics.
Автор: Pelillo Название: Artificial Life and Evolutionary Computation ISBN: 3319786571 ISBN-13(EAN): 9783319786575 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the revised selected papers of the 12th Italian Workshop on Advances in Artificial Life, Evolutionary Computation, WIVACE 2017, held in Venice, Italy, in September 2017.The 23 full papers presented were thoroughly reviewed and selected from 33 submissions. They cover the following topics: physical-chemical phenomena;
Автор: Manuel Barros; Jorge Guilherme; Nuno Horta Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques ISBN: 3642263232 ISBN-13(EAN): 9783642263231 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
Автор: Xin-She Yang Название: Recent Advances in Swarm Intelligence and Evolutionary Computation ISBN: 3319138251 ISBN-13(EAN): 9783319138251 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recent Advances in Swarm Intelligence and Evolutionary Computation
Автор: Clara Pizzuti; Giandomenico Spezzano Название: Advances in Artificial Life and Evolutionary Computation ISBN: 3319127446 ISBN-13(EAN): 9783319127446 Издательство: Springer Рейтинг: Цена: 54040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial neural networks.- Fuzzy inference systems.- Rough set.- Approximate reasoning.- Optimization methods such as evolutionary computation, swarm intelligence, particle swarm optimization.
Автор: Christian Blum; Gabriela Ochoa Название: Evolutionary Computation in Combinatorial Optimization ISBN: 3662443198 ISBN-13(EAN): 9783662443194 Издательство: Springer Рейтинг: Цена: 44720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A Hybrid Ant Colony Optimization Algorithm for the Far From Most String Problem.- A Parametric Framework for Cooperative Parallel Local Search.- A Survey of Meta-heuristics Used for Computing Maximin Latin Hypercube.- An Analysis of Parameters of irace.- An Improved Multi-objective Algorithm for the Urban Transit Routing Problem.- An Iterated Greedy Heuristic for Simultaneous Lot-Sizing and Scheduling Problem in Production Flow Shop Environments.- Balancing Bicycle Sharing Systems: An Approach for the Dynamic Case.- Cooperative Selection: Improving Tournament Selection via Altruism.- Diversity-Driven Selection of Multiple Crossover Operators for the Capacitated Arc Routing Problem.- Dynamic Period Routing for a Complex Real-World System: A Case Study in Storm Drain Maintenance.- Elementary Landscape Decomposition of the Hamiltonian Path Optimization Problem.- Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection Global Optimization of Multimodal Deceptive Functions.- Learning Inherent Networks from Stochastic Search Methods.- Metaheuristics for the Pick-Up and Delivery Problem with Contracted Orders.- Modeling an Artificial Bee Colony with Inspector for Clustering Tasks.- Personalized Multi-day Trips to Touristic Regions: A Hybrid GA-VND Approach.- Phase Transition and Landscape Properties of the Number Partitioning Problem.- The Firefighter Problem: Application of Hybrid Ant Colony Optimization Algorithms.- The Influence of Correlated Objectives on Different Types of P-ACO Algorithms.
A Parametric Framework for Cooperative Parallel Local Search.- A Survey of Meta-heuristics Used for Computing Maximin Latin Hypercube.- An Analysis of Parameters of irace.- An Improved Multi-objective Algorithm for the Urban Transit Routing Problem.- An Iterated Greedy Heuristic for Simultaneous Lot-Sizing and Scheduling Problem in Production Flow Shop Environments.- Balancing Bicycle Sharing Systems: An Approach for the Dynamic Case.- Cooperative Selection: Improving Tournament Selection via Altruism.- Diversity-Driven Selection of Multiple Crossover Operators for the Capacitated Arc Routing Problem.- Dynamic Period Routing for a Complex Real-World System: A Case Study in Storm Drain Maintenance.- Elementary Landscape Decomposition of the Hamiltonian Path Optimization Problem.- Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection Global Optimization of Multimodal Deceptive Functions.- Learning Inherent Networks from Stochastic Search Methods.- Metaheuristics for the Pick-Up and Delivery Problem with Contracted Orders.- Modeling an Artificial Bee Colony with Inspector for Clustering Tasks.- Personalized Multi-day Trips to Touristic Regions: A Hybrid GA-VND Approach.- Phase Transition and Landscape Properties of the Number Partitioning Problem.- The Firefighter Problem: Application of Hybrid Ant Colony Optimization Algorithms.- The Influence of Correlated Objectives on Different Types of P-ACO Algorithms.
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