Evolutionary Multi-Criterion Optimization / Trautmann, Heike ; Rudolph, Günter ; Klamroth, Kathrin ; Schütze, Oliver ; Wiecek, Margaret ; Jin, Yaochu ; Grimme, Christian  

Público ISBD Ningún comentario para este registro. Título : Evolutionary Multi-Criterion Optimization : 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings Tipo de documento: documento electrónico Autores: Trautmann, Heike, ; Rudolph, Günter, ; Klamroth, Kathrin, ; Schütze, Oliver, ; Wiecek, Margaret, ; Jin, Yaochu, ; Grimme, Christian, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIV, 702 p. 267 ilustraciones ISBN/ISSN/DL: 978-3-319-54157-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Análisis numérico  Algoritmos  Ciencias de la Computación  Inteligencia artificial  Red de computadoras  Modelos de Computación  Redes de comunicación informática Clasificación: 518 Resumen: Este libro constituye las actas arbitradas de la 9.ª Conferencia Internacional sobre Optimización Evolutiva de Criterios Múltiples, EMO 2017, celebrada en Münster, Alemania, en marzo de 2017. Los 33 artículos completos revisados ​​presentados junto con 13 presentaciones de carteles fueron cuidadosamente revisados ​​y seleccionados entre 72 presentaciones. La EMO 2017 tiene como objetivo discutir todos los aspectos del desarrollo y despliegue de EMO, incluidos los fundamentos teóricos; técnicas de manejo de restricciones; técnicas de manejo de preferencias; manejo de problemas continuos, combinatorios o de enteros mixtos; técnicas de búsqueda local; enfoques híbridos; criterios de parada; modelos EMO paralelos; Evaluación del desempeño; funciones de prueba y problemas de referencia; enfoques de selección de algoritmos; optimización de muchos objetivos; optimización a gran escala; aplicaciones del mundo real; Implementaciones del algoritmo EMO. Nota de contenido: On the effect of scalarising norm choice in a ParEGO implementation -- Multi-objective big data optimization with Metal and Spark -- An empirical assessment of the properties of inverted generational distance indicators on multi- and many-objective optimization -- Solving the Bi-objective traveling thief problem with multi-objective evolutionary algorithms -- Automatically Configuring multi-objective local search using multi-objective optimization -- The multi-objective shortest path problem is NP-hard, or is it -- Angle-based preference models in multi-objective optimization -- Quantitative performance assessment of multi-objective optimizers: The average runtime attainment function -- A multi-objective strategy to allocate roadside units in a vehicular network with guaranteed levels of service -- An approach for the local exploration of discrete many objective optimization problems -- A note on the detection of outliers in a binary outranking relation -- Classifying meta-modeling methodologiesfor evolutionary multi-objective optimization: First results -- Weighted stress function method for multi-objective evolutionary algorithm based on decomposition -- Timing the decision support for real-world many-objective problems -- On the influence of altering the action set on PROMETHEE II's relative ranks -- Peek { Shape { Grab: a methodology in three stages for approximating the non-dominated points of multi-objective discrete combinatorial optimization problems with a multi-objective meta-heuristic -- A new reduced-length genetic representation for evolutionary multi-objective clustering -- A fast incremental BSP tree archive for non-dominated points -- Adaptive operator selection for many-objective optimization with NSGA-III -- On using decision maker preferences with ParEGO -- First investigations on noisy model-based multi-objective optimization -- Fusion of many-objective non-dominated solutions using reference points -- An expedition to multi-modal multi-objective optimization landscapes -- Neutral neighbors in Bi-objective optimization: Distribution of the most promising for permutation problems -- Multi-objective adaptation of a parameterized GVGAI agent towards several games -- Towards standardized and seamless integration of expert knowledge into multi-objective evolutionary optimization algorithms -- Empirical investigations of reference point based methods when facing a massively large number of objectives: First results -- Building and using an ontology of preference-based multi-objective evolutionary algorithms -- A fitness landscape analysis of pareto local search on Bi-objective permutation flow-shop scheduling problems -- Dimensionality reduction approach for many-objective vehicle routing problem with demand responsive transport -- Heterogeneous evolutionary swarms with partial redundancy solving multi-objective tasks -- Multiple meta-models for robustness estimation in multi-objective robust optimization -- Predator-Prey techniques for solving multi-objective scheduling problems for unrelated parallel machines -- An overview of weighted and unconstrained scalarizing functions -- Multi-objective representation setups for deformation-based design optimization -- Design perspectives of an evolutionary process for multi-objective molecular optimization -- Towards a better balance of diversity and convergence in NSGA-III: First results -- A comparative study of fast adaptive preference-guided evolutionary multi-objective optimization -- A population-based algorithm for learning a majority rule sorting model with coalitional veto -- Injection of extreme points in evolutionary multio-objective optimization algorithms -- The impact of population size, number of children, and number of reference points on the performance of NSGA-III -- Multi-objective optimization for liner shipping fleet repositioning -- Surrogate-assisted partial order-based evolutionary optimization -- Hyper-volume indicator gradient ascent multi-objective optimization -- Toward step-size adaptation in evolutionary multi-objective optimization -- Computing 3-D expected hyper-volume improvement and related integrals in asymptotically optimal time. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017. The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Evolutionary Multi-Criterion Optimization : 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings [documento electrónico] / Trautmann, Heike, ; Rudolph, Günter, ; Klamroth, Kathrin, ; Schütze, Oliver, ; Wiecek, Margaret, ; Jin, Yaochu, ; Grimme, Christian,  . -  1 ed.  . - [s.l.] : Springer, 2017 . - XIV, 702 p. 267 ilustraciones. ISBN : 978-3-319-54157-0 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Análisis numérico  Algoritmos  Ciencias de la Computación  Inteligencia artificial  Red de computadoras  Modelos de Computación  Redes de comunicación informática Clasificación: 518 Resumen: Este libro constituye las actas arbitradas de la 9.ª Conferencia Internacional sobre Optimización Evolutiva de Criterios Múltiples, EMO 2017, celebrada en Münster, Alemania, en marzo de 2017. Los 33 artículos completos revisados ​​presentados junto con 13 presentaciones de carteles fueron cuidadosamente revisados ​​y seleccionados entre 72 presentaciones. La EMO 2017 tiene como objetivo discutir todos los aspectos del desarrollo y despliegue de EMO, incluidos los fundamentos teóricos; técnicas de manejo de restricciones; técnicas de manejo de preferencias; manejo de problemas continuos, combinatorios o de enteros mixtos; técnicas de búsqueda local; enfoques híbridos; criterios de parada; modelos EMO paralelos; Evaluación del desempeño; funciones de prueba y problemas de referencia; enfoques de selección de algoritmos; optimización de muchos objetivos; optimización a gran escala; aplicaciones del mundo real; Implementaciones del algoritmo EMO. Nota de contenido: On the effect of scalarising norm choice in a ParEGO implementation -- Multi-objective big data optimization with Metal and Spark -- An empirical assessment of the properties of inverted generational distance indicators on multi- and many-objective optimization -- Solving the Bi-objective traveling thief problem with multi-objective evolutionary algorithms -- Automatically Configuring multi-objective local search using multi-objective optimization -- The multi-objective shortest path problem is NP-hard, or is it -- Angle-based preference models in multi-objective optimization -- Quantitative performance assessment of multi-objective optimizers: The average runtime attainment function -- A multi-objective strategy to allocate roadside units in a vehicular network with guaranteed levels of service -- An approach for the local exploration of discrete many objective optimization problems -- A note on the detection of outliers in a binary outranking relation -- Classifying meta-modeling methodologiesfor evolutionary multi-objective optimization: First results -- Weighted stress function method for multi-objective evolutionary algorithm based on decomposition -- Timing the decision support for real-world many-objective problems -- On the influence of altering the action set on PROMETHEE II's relative ranks -- Peek { Shape { Grab: a methodology in three stages for approximating the non-dominated points of multi-objective discrete combinatorial optimization problems with a multi-objective meta-heuristic -- A new reduced-length genetic representation for evolutionary multi-objective clustering -- A fast incremental BSP tree archive for non-dominated points -- Adaptive operator selection for many-objective optimization with NSGA-III -- On using decision maker preferences with ParEGO -- First investigations on noisy model-based multi-objective optimization -- Fusion of many-objective non-dominated solutions using reference points -- An expedition to multi-modal multi-objective optimization landscapes -- Neutral neighbors in Bi-objective optimization: Distribution of the most promising for permutation problems -- Multi-objective adaptation of a parameterized GVGAI agent towards several games -- Towards standardized and seamless integration of expert knowledge into multi-objective evolutionary optimization algorithms -- Empirical investigations of reference point based methods when facing a massively large number of objectives: First results -- Building and using an ontology of preference-based multi-objective evolutionary algorithms -- A fitness landscape analysis of pareto local search on Bi-objective permutation flow-shop scheduling problems -- Dimensionality reduction approach for many-objective vehicle routing problem with demand responsive transport -- Heterogeneous evolutionary swarms with partial redundancy solving multi-objective tasks -- Multiple meta-models for robustness estimation in multi-objective robust optimization -- Predator-Prey techniques for solving multi-objective scheduling problems for unrelated parallel machines -- An overview of weighted and unconstrained scalarizing functions -- Multi-objective representation setups for deformation-based design optimization -- Design perspectives of an evolutionary process for multi-objective molecular optimization -- Towards a better balance of diversity and convergence in NSGA-III: First results -- A comparative study of fast adaptive preference-guided evolutionary multi-objective optimization -- A population-based algorithm for learning a majority rule sorting model with coalitional veto -- Injection of extreme points in evolutionary multio-objective optimization algorithms -- The impact of population size, number of children, and number of reference points on the performance of NSGA-III -- Multi-objective optimization for liner shipping fleet repositioning -- Surrogate-assisted partial order-based evolutionary optimization -- Hyper-volume indicator gradient ascent multi-objective optimization -- Toward step-size adaptation in evolutionary multi-objective optimization -- Computing 3-D expected hyper-volume improvement and related integrals in asymptotically optimal time. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017. The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

Rescheduling Under Disruptions in Manufacturing Systems / Wang, Dujuan  

Público ISBD Ningún comentario para este registro. Título : Rescheduling Under Disruptions in Manufacturing Systems : Models and Algorithms Tipo de documento: documento electrónico Autores: Wang, Dujuan, ; Yin, Yunqiang, ; Jin, Yaochu, Mención de edición: 1 ed. Editorial: Singapore [Malasia] : Springer Fecha de publicación: 2020 Número de páginas: XI, 147 p. 15 ilustraciones, 11 ilustraciones en color. ISBN/ISSN/DL: 978-981-1535284-- Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Econometría  Sociología  Economía cuantitativa  Métodos sociológicos Clasificación: 330.9 Economía (Situación y condiciones económicas) Resumen: Este libro proporciona una introducción a los modelos, métodos y resultados de algunos problemas de reprogramación en presencia de eventos de interrupción inesperados, incluida la indisponibilidad de puestos de trabajo, la llegada de nuevos puestos de trabajo y averías de máquinas. La ocurrencia de estas interrupciones inesperadas puede causar un cambio en el cronograma planificado, lo que puede hacer que el cronograma originalmente viable no sea factible. La reprogramación, que implica ajustar el cronograma original para tener en cuenta una interrupción, es necesaria para minimizar los efectos de la interrupción en el desempeño del sistema. Esto implica un equilibrio entre encontrar un nuevo cronograma rentable y evitar cambios excesivos en el cronograma original. Este libro considera la teoría de la programación como una teoría práctica y se ha asegurado de enfatizar los aspectos prácticos de su cobertura temática. Por lo tanto, este libro considera algunos escenarios existentes en la mayoría de los entornos del mundo real, como el mantenimiento preventivo de la máquina y el efecto de deterioro en el que el tiempo de procesamiento real de un trabajo aumenta junto con el uso y la antigüedad de la máquina. Para aliviar el efecto de los eventos de disrupción, se adoptan algunas estrategias flexibles, incluida la asignación de recursos adicionales para reducir los tiempos de procesamiento de los trabajos o el rechazo de la producción de algunos trabajos. Para cada escenario considerado, dependiendo de la configuración del modelo y de los eventos de interrupción, este libro aborda la complejidad y el diseño de algoritmos eficientes, exactos o aproximados. Especialmente cuando los métodos de optimización y las herramientas analíticas no son suficientes, este libro enfatiza las metaheurísticas, incluido el algoritmo genético de clasificación elitista no dominado mejorado y el algoritmo de evolución diferencial. Este libro también proporciona extensos estudios numéricos para evaluar el rendimiento de los algoritmos propuestos. El problema de la reprogramación en presencia de eventos de interrupción inesperados es de gran importancia para la implementación exitosa de sistemas de programación del mundo real. Actualmente existe un asombroso conjunto de conocimientos en este campo. Este libro es la primera monografía sobre la reprogramación. Su objetivo es presentar los logros de investigación del autor en materia de reprogramación. Está escrito para investigadores y Ph.D. estudiantes que trabajan en teoría de programación y otros miembros de la comunidad científica que estén interesados ​​en modelos de programación recientes. Nuestro objetivo es permitir al lector conocer algunos nuevos logros en este tema. Nota de contenido: Chapter 1 Introduction -- Chapter 2 Rescheduling on identical parallel machines in the presence of machine breakdowns -- Chapter 3 Parallel-machine rescheduling with job rejection in the presence of job unavailability -- Chapter 4 Rescheduling with controllable processing times and job rejection in the presence of new arrival jobs and deterioration eect -- Chapter 5 Rescheduling with controllable processing times and preventive maintenance in the presence of new arrival jobs and deterioration eect -- Chapter 6 A knowledge-based evolutionary proactive scheduling approach in the presence of ma-chine breakdown and deterioration eect. Tipo de medio : Computadora Summary : This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule. This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine's usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Rescheduling Under Disruptions in Manufacturing Systems : Models and Algorithms [documento electrónico] / Wang, Dujuan, ; Yin, Yunqiang, ; Jin, Yaochu,  . -  1 ed.  . - Singapore [Malasia] : Springer, 2020 . - XI, 147 p. 15 ilustraciones, 11 ilustraciones en color. ISBN : 978-981-1535284-- Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Econometría  Sociología  Economía cuantitativa  Métodos sociológicos Clasificación: 330.9 Economía (Situación y condiciones económicas) Resumen: Este libro proporciona una introducción a los modelos, métodos y resultados de algunos problemas de reprogramación en presencia de eventos de interrupción inesperados, incluida la indisponibilidad de puestos de trabajo, la llegada de nuevos puestos de trabajo y averías de máquinas. La ocurrencia de estas interrupciones inesperadas puede causar un cambio en el cronograma planificado, lo que puede hacer que el cronograma originalmente viable no sea factible. La reprogramación, que implica ajustar el cronograma original para tener en cuenta una interrupción, es necesaria para minimizar los efectos de la interrupción en el desempeño del sistema. Esto implica un equilibrio entre encontrar un nuevo cronograma rentable y evitar cambios excesivos en el cronograma original. Este libro considera la teoría de la programación como una teoría práctica y se ha asegurado de enfatizar los aspectos prácticos de su cobertura temática. Por lo tanto, este libro considera algunos escenarios existentes en la mayoría de los entornos del mundo real, como el mantenimiento preventivo de la máquina y el efecto de deterioro en el que el tiempo de procesamiento real de un trabajo aumenta junto con el uso y la antigüedad de la máquina. Para aliviar el efecto de los eventos de disrupción, se adoptan algunas estrategias flexibles, incluida la asignación de recursos adicionales para reducir los tiempos de procesamiento de los trabajos o el rechazo de la producción de algunos trabajos. Para cada escenario considerado, dependiendo de la configuración del modelo y de los eventos de interrupción, este libro aborda la complejidad y el diseño de algoritmos eficientes, exactos o aproximados. Especialmente cuando los métodos de optimización y las herramientas analíticas no son suficientes, este libro enfatiza las metaheurísticas, incluido el algoritmo genético de clasificación elitista no dominado mejorado y el algoritmo de evolución diferencial. Este libro también proporciona extensos estudios numéricos para evaluar el rendimiento de los algoritmos propuestos. El problema de la reprogramación en presencia de eventos de interrupción inesperados es de gran importancia para la implementación exitosa de sistemas de programación del mundo real. Actualmente existe un asombroso conjunto de conocimientos en este campo. Este libro es la primera monografía sobre la reprogramación. Su objetivo es presentar los logros de investigación del autor en materia de reprogramación. Está escrito para investigadores y Ph.D. estudiantes que trabajan en teoría de programación y otros miembros de la comunidad científica que estén interesados ​​en modelos de programación recientes. Nuestro objetivo es permitir al lector conocer algunos nuevos logros en este tema. Nota de contenido: Chapter 1 Introduction -- Chapter 2 Rescheduling on identical parallel machines in the presence of machine breakdowns -- Chapter 3 Parallel-machine rescheduling with job rejection in the presence of job unavailability -- Chapter 4 Rescheduling with controllable processing times and job rejection in the presence of new arrival jobs and deterioration eect -- Chapter 5 Rescheduling with controllable processing times and preventive maintenance in the presence of new arrival jobs and deterioration eect -- Chapter 6 A knowledge-based evolutionary proactive scheduling approach in the presence of ma-chine breakdown and deterioration eect. Tipo de medio : Computadora Summary : This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule. This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine's usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

Simulated Evolution and Learning / Shi, Yuhui ; Tan, Kay Chen ; Zhang, Mengjie ; Tang, Ke ; Li, Xiaodong ; Zhang, Qingfu ; Tan, Ying ; Middendorf, Martin ; Jin, Yaochu  

Público ISBD Ningún comentario para este registro. Título : Simulated Evolution and Learning : 11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings / Tipo de documento: documento electrónico Autores: Shi, Yuhui, ; Tan, Kay Chen, ; Zhang, Mengjie, ; Tang, Ke, ; Li, Xiaodong, ; Zhang, Qingfu, ; Tan, Ying, ; Middendorf, Martin, ; Jin, Yaochu, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XXII, 1041 p. 317 ilustraciones ISBN/ISSN/DL: 978-3-319-68759-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Ciencias de la Computación  Inteligencia artificial  Algoritmos  Red de computadoras  Simulación por ordenador  Teoría de la Computación  Modelos de Computación  Redes de comunicación informática  Modelado por computadora Clasificación: 40.151 Resumen: Este libro constituye las actas arbitradas de la 11.ª Conferencia Internacional sobre Evolución y Aprendizaje Simulado, SEAL 2017, celebrada en Shenzhen, China, en noviembre de 2017. Los 85 artículos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 145 presentaciones. Estaban organizados en secciones temáticas denominadas: optimización evolutiva; optimización evolutiva multiobjetivo; aprendizaje automático evolutivo; desarrollos teóricos; selección de características y reducción de dimensionalidad; entornos dinámicos e inciertos; aplicaciones del mundo real; sistemas adaptativos; e inteligencia de enjambre. Nota de contenido: Evolutionary Optimisation -- Maximum Likelihood Estimation based on Random Subspace EDA: Application to Extrasolar Planet Detection -- Evolutionary Games Network Reconstruction by Memetic Algorithm with l1/2 Regularization -- A Simple Brain Storm Optimization Algorithm via Visualizing Confidence Intervals -- Simulated Annealing with a Time-slot Heuristic for Ready-mix Concrete Delivery -- A Sequential Learnable Evolutionary Algorithm with a Novel Knowledge Base Generation Method -- Using Parallel Strategies to Speed Up Pareto Local Search -- Differential evolution based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time -- ACO-iRBA: A Hybrid Approach to TSPN with Overlapping Neighborhoods -- An Evolutionary Algorithm with A New Coding Scheme for Multi-objective Portfolio Optimization -- Exact Approaches for the Travelling Thief Problem -- On the Use of Dynamic Reference Points in HypE -- Multi-Factorial Evolutionary Algorithm Based on M2M Decomposition -- An Efficient Local Search Algorithm for Minimum Weighted Vertex Cover on Massive Graphs -- Interactive Genetic Algorithm with Group Intelligence Articulated Possibilistic Condition Preference Model -- GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition -- Matrix Factorization based Benchmark Set Analysis: A Case Study on HyFlex.-Learning to Describe Collective Search Behavior of Evolutionary Algorithms in Solution Space -- Evolutionary Multiobjective Optimisation -- A Hierarchical Decomposition-based Evolutionary Many-objective Algorithm -- Adjusting Parallel Coordinates for Investigating Multi-Objective Search -- An Elite Archive-based MOEA/D Algorithm -- A constraint partitioning method based on minimax strategy for constrained multiobjective optimization problems -- A Fast Objective Reduction Algorithm based on Dominance Structure for Many Objective Optimization -- A memetic algorithm based on decomposition and extended search for Multi-Objective CapacitatedArc Routing Problem -- Improvement of reference points for decomposition based multi-objective evolutionary algorithms -- Multi-Objective Evolutionary Optimization for Autonomous Intersection Management -- Study of an adaptive control of aggregate functions in MOEA/D -- Use of Inverted Triangular Weight Vectors in Decomposition-Based Many-Objective Algorithms -- Surrogate Model Assisted Multi-Objective Differential Evolution Algorithm for Performance Optimization at Software Architecture Level -- Normalized Ranking Based Particle Swarm Optimizer for Many Objective Optimization -- Evolutionary Machine Learning -- A Study on Pre-Training Deep Neural Networks Using Particle Swarm Optimisation -- Simple Linkage Identification Using Genetic Clustering -- Learning of Sparse Fuzzy Cognitive Maps Using Evolutionary Algorithm with Lasso Initialization -- A Bayesian Restarting Approach to Algorithm Selection -- Evolutionary Learning based Iterated Local Search for Google Machine Reassignment Problems -- Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression -- Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling -- Visualisation and Optimisation of Learning Classifier Systems for Multiple Domain Learning -- Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-objective Optimization -- Effective Policy Gradient Search for Reinforcement Learning through NEAT based Feature Extraction -- Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation -- A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors -- Theoretical Developments -- Running-time Analysis of Particle Swarm Optimization with a Single Particle Based on Average Gain -- Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey -- Feature Selection and Dimensionality Reduction -- New Representations in Genetic Programming for Feature Construction in k-means Clustering -- Transductive Transfer Learning in Genetic Programming for Document Classification -- Automatic Feature Construction for Network Intrusion Detection -- A Feature Subset Evaluation Method based on Multi-objective Optimization -- A Hybrid GA-GP Method for Feature Reduction in Classification -- Kernel Construction and Feature Subset Selection in Support Vector Machines -- KW-Race and Fast KW-Race: Racing-based Frameworks for Tuning Parameters of Evolutionary Algorithms on Black-box Optimization Problems -- Dynamic and Uncertain Environments -- A Probabilistic Learning Algorithm for the Shortest Path Problem -- A first-order difference model-based evolutionary dynamic multiobjective optimization -- A Construction Graph-based Evolutionary Algorithm For Traveling Salesman Problem -- Real-world Applications -- Bi-objective water cycle algorithm for solving remanufacturing rescheduling problem -- A New Method for Constructing Ensemble Classifier in Privacy-Preserving Distributed Environment -- Greedy based Pareto Local Search for Bi-objective Robust Airport Gate Assignment Problem -- Multi-neighbourhood Great Deluge for Google Machine Reassignment Problem -- Evolutionary Optimization of Airport Security Inspection Allocation -- Evolving Directional Changes Trading Strategies with a New Event-based Indicator -- Constrained Differential Evolution for Cost and Energy Efficiency Optimization in 5G Wireless Networks -- Evolutionary Computation to Determine Product Builds in Open Pit Mining -- An Evolutionary Vulnerability Detection Method for HFSWR Ship Tracking Algorithm -- Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with Mobile Sink -- Unsupervised Change Detection for Remote Sensing Images Based on Principal Component Analysis and Differential Evolution -- Parallel particle swarm optimization for community detection in large-scale networks -- Multi-objective memetic algorithm based onthree-dimentional request prediction for dynamic pickup-and-delivery problem with time windows -- Optimization of Spectrum-Energy Efficiency in Heterogeneous Communication Network -- Large scale WSN deployment based on an improved cooperative coevolutionary PSO with global differential grouping -- Adaptive Systems -- Learning Fuzzy Cognitive Maps Using a Genetic Algorithm with Decision-making Trial and Evaluation -- Dynamic and Adaptive Threshold for DNN Compression from Scratch -- Cooperative Design of Two Level Fuzzy Logic Controllers for Medium Access Control in Wireless Body Area Networks -- Statistical Analysis of Social Coding in GitHub Hypernetwork -- Swarm Intelligence -- Sparse Restricted Boltzmann Machine Based on Multiobjective Optimization -- A Knee Point Driven Particle Swarm Optimization Algorithm for Sparse Reconstruction -- Multivariant optimization algorithm with bimodal-gauss -- Enhanced Comprehensive Learning Particle Swarm Optimization with Exemplar Evolution -- Recommending PSOvariants using meta-learning framework for global optimization -- Augmented Brain Storm Optimization with Mutation Strategies -- A new precedence-based Ant Colony Optimization for permutation problems -- A general swarm intelligence model for continuous function optimization -- A Hybrid Particle Swarm Optimization for High-Dimensional Dynamic Optimization -- Visualizing the Search Dynamics in a High-dimensional Space for a Particle Swarm Optimizer -- Particle Swarm Optimization with Winning Score Assignment for Multi-objective Portfolio Optimization -- Conservatism and Adventurism in Particle Swarm Optimization Algorithm -- A competitive social spider optimization with learning strategy for PID controller optimization.  . Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017, held in Shenzhen, China, in November 2017.  The 85 papers presented in this volume were carefully reviewed and selected from 145 submissions. They were organized in topical sections named: evolutionary optimisation; evolutionary multiobjective optimisation; evolutionary machine learning; theoretical developments; feature selection and dimensionality reduction; dynamic and uncertain environments; real-world applications; adaptive systems; and swarm intelligence. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Simulated Evolution and Learning : 11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings / [documento electrónico] / Shi, Yuhui, ; Tan, Kay Chen, ; Zhang, Mengjie, ; Tang, Ke, ; Li, Xiaodong, ; Zhang, Qingfu, ; Tan, Ying, ; Middendorf, Martin, ; Jin, Yaochu,  . -  1 ed.  . - [s.l.] : Springer, 2017 . - XXII, 1041 p. 317 ilustraciones. ISBN : 978-3-319-68759-9 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Ciencias de la Computación  Inteligencia artificial  Algoritmos  Red de computadoras  Simulación por ordenador  Teoría de la Computación  Modelos de Computación  Redes de comunicación informática  Modelado por computadora Clasificación: 40.151 Resumen: Este libro constituye las actas arbitradas de la 11.ª Conferencia Internacional sobre Evolución y Aprendizaje Simulado, SEAL 2017, celebrada en Shenzhen, China, en noviembre de 2017. Los 85 artículos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 145 presentaciones. Estaban organizados en secciones temáticas denominadas: optimización evolutiva; optimización evolutiva multiobjetivo; aprendizaje automático evolutivo; desarrollos teóricos; selección de características y reducción de dimensionalidad; entornos dinámicos e inciertos; aplicaciones del mundo real; sistemas adaptativos; e inteligencia de enjambre. Nota de contenido: Evolutionary Optimisation -- Maximum Likelihood Estimation based on Random Subspace EDA: Application to Extrasolar Planet Detection -- Evolutionary Games Network Reconstruction by Memetic Algorithm with l1/2 Regularization -- A Simple Brain Storm Optimization Algorithm via Visualizing Confidence Intervals -- Simulated Annealing with a Time-slot Heuristic for Ready-mix Concrete Delivery -- A Sequential Learnable Evolutionary Algorithm with a Novel Knowledge Base Generation Method -- Using Parallel Strategies to Speed Up Pareto Local Search -- Differential evolution based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time -- ACO-iRBA: A Hybrid Approach to TSPN with Overlapping Neighborhoods -- An Evolutionary Algorithm with A New Coding Scheme for Multi-objective Portfolio Optimization -- Exact Approaches for the Travelling Thief Problem -- On the Use of Dynamic Reference Points in HypE -- Multi-Factorial Evolutionary Algorithm Based on M2M Decomposition -- An Efficient Local Search Algorithm for Minimum Weighted Vertex Cover on Massive Graphs -- Interactive Genetic Algorithm with Group Intelligence Articulated Possibilistic Condition Preference Model -- GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition -- Matrix Factorization based Benchmark Set Analysis: A Case Study on HyFlex.-Learning to Describe Collective Search Behavior of Evolutionary Algorithms in Solution Space -- Evolutionary Multiobjective Optimisation -- A Hierarchical Decomposition-based Evolutionary Many-objective Algorithm -- Adjusting Parallel Coordinates for Investigating Multi-Objective Search -- An Elite Archive-based MOEA/D Algorithm -- A constraint partitioning method based on minimax strategy for constrained multiobjective optimization problems -- A Fast Objective Reduction Algorithm based on Dominance Structure for Many Objective Optimization -- A memetic algorithm based on decomposition and extended search for Multi-Objective CapacitatedArc Routing Problem -- Improvement of reference points for decomposition based multi-objective evolutionary algorithms -- Multi-Objective Evolutionary Optimization for Autonomous Intersection Management -- Study of an adaptive control of aggregate functions in MOEA/D -- Use of Inverted Triangular Weight Vectors in Decomposition-Based Many-Objective Algorithms -- Surrogate Model Assisted Multi-Objective Differential Evolution Algorithm for Performance Optimization at Software Architecture Level -- Normalized Ranking Based Particle Swarm Optimizer for Many Objective Optimization -- Evolutionary Machine Learning -- A Study on Pre-Training Deep Neural Networks Using Particle Swarm Optimisation -- Simple Linkage Identification Using Genetic Clustering -- Learning of Sparse Fuzzy Cognitive Maps Using Evolutionary Algorithm with Lasso Initialization -- A Bayesian Restarting Approach to Algorithm Selection -- Evolutionary Learning based Iterated Local Search for Google Machine Reassignment Problems -- Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression -- Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling -- Visualisation and Optimisation of Learning Classifier Systems for Multiple Domain Learning -- Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-objective Optimization -- Effective Policy Gradient Search for Reinforcement Learning through NEAT based Feature Extraction -- Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation -- A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors -- Theoretical Developments -- Running-time Analysis of Particle Swarm Optimization with a Single Particle Based on Average Gain -- Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey -- Feature Selection and Dimensionality Reduction -- New Representations in Genetic Programming for Feature Construction in k-means Clustering -- Transductive Transfer Learning in Genetic Programming for Document Classification -- Automatic Feature Construction for Network Intrusion Detection -- A Feature Subset Evaluation Method based on Multi-objective Optimization -- A Hybrid GA-GP Method for Feature Reduction in Classification -- Kernel Construction and Feature Subset Selection in Support Vector Machines -- KW-Race and Fast KW-Race: Racing-based Frameworks for Tuning Parameters of Evolutionary Algorithms on Black-box Optimization Problems -- Dynamic and Uncertain Environments -- A Probabilistic Learning Algorithm for the Shortest Path Problem -- A first-order difference model-based evolutionary dynamic multiobjective optimization -- A Construction Graph-based Evolutionary Algorithm For Traveling Salesman Problem -- Real-world Applications -- Bi-objective water cycle algorithm for solving remanufacturing rescheduling problem -- A New Method for Constructing Ensemble Classifier in Privacy-Preserving Distributed Environment -- Greedy based Pareto Local Search for Bi-objective Robust Airport Gate Assignment Problem -- Multi-neighbourhood Great Deluge for Google Machine Reassignment Problem -- Evolutionary Optimization of Airport Security Inspection Allocation -- Evolving Directional Changes Trading Strategies with a New Event-based Indicator -- Constrained Differential Evolution for Cost and Energy Efficiency Optimization in 5G Wireless Networks -- Evolutionary Computation to Determine Product Builds in Open Pit Mining -- An Evolutionary Vulnerability Detection Method for HFSWR Ship Tracking Algorithm -- Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with Mobile Sink -- Unsupervised Change Detection for Remote Sensing Images Based on Principal Component Analysis and Differential Evolution -- Parallel particle swarm optimization for community detection in large-scale networks -- Multi-objective memetic algorithm based onthree-dimentional request prediction for dynamic pickup-and-delivery problem with time windows -- Optimization of Spectrum-Energy Efficiency in Heterogeneous Communication Network -- Large scale WSN deployment based on an improved cooperative coevolutionary PSO with global differential grouping -- Adaptive Systems -- Learning Fuzzy Cognitive Maps Using a Genetic Algorithm with Decision-making Trial and Evaluation -- Dynamic and Adaptive Threshold for DNN Compression from Scratch -- Cooperative Design of Two Level Fuzzy Logic Controllers for Medium Access Control in Wireless Body Area Networks -- Statistical Analysis of Social Coding in GitHub Hypernetwork -- Swarm Intelligence -- Sparse Restricted Boltzmann Machine Based on Multiobjective Optimization -- A Knee Point Driven Particle Swarm Optimization Algorithm for Sparse Reconstruction -- Multivariant optimization algorithm with bimodal-gauss -- Enhanced Comprehensive Learning Particle Swarm Optimization with Exemplar Evolution -- Recommending PSOvariants using meta-learning framework for global optimization -- Augmented Brain Storm Optimization with Mutation Strategies -- A new precedence-based Ant Colony Optimization for permutation problems -- A general swarm intelligence model for continuous function optimization -- A Hybrid Particle Swarm Optimization for High-Dimensional Dynamic Optimization -- Visualizing the Search Dynamics in a High-dimensional Space for a Particle Swarm Optimizer -- Particle Swarm Optimization with Winning Score Assignment for Multi-objective Portfolio Optimization -- Conservatism and Adventurism in Particle Swarm Optimization Algorithm -- A competitive social spider optimization with learning strategy for PID controller optimization.  . Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017, held in Shenzhen, China, in November 2017.  The 85 papers presented in this volume were carefully reviewed and selected from 145 submissions. They were organized in topical sections named: evolutionary optimisation; evolutionary multiobjective optimisation; evolutionary machine learning; theoretical developments; feature selection and dimensionality reduction; dynamic and uncertain environments; real-world applications; adaptive systems; and swarm intelligence. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

Towards Autonomous Robotic Systems / Gao, Yang ; Fallah, Saber ; Jin, Yaochu ; Lekakou, Constantina  

Público ISBD Ningún comentario para este registro. Título : Towards Autonomous Robotic Systems : 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings / Tipo de documento: documento electrónico Autores: Gao, Yang, ; Fallah, Saber, ; Jin, Yaochu, ; Lekakou, Constantina, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIII, 705 p. 400 ilustraciones ISBN/ISSN/DL: 978-3-319-64107-2 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial  Computadoras  Propósitos especiales  Red de computadoras  Interfaces de usuario (sistemas informáticos)  La interacción persona-ordenador  Sistemas de propósito especial y basados ​​en aplicaciones  Redes de comunicación informática  Interfaces de usuario e interacción persona-computadora Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas de la 18.ª Conferencia Anual sobre la Robótica Autónoma, TAROS 2017, celebrada en Guildford, Reino Unido, en julio de 2017. Los 43 artículos completos revisados ​​presentados junto con 13 artículos breves fueron cuidadosamente revisados ​​y seleccionados entre 66 presentaciones. Los artículos analizan la investigación en robótica extraída de una amplia y diversa gama de temas, como sistemas enjambres y multirobóticos; interacción humano-robot; aprendizaje e imitación robótica; navegación, planificación y seguridad de robots; robots humanoides y bioinspirados; robots y vehículos móviles; pruebas y diseño de robots; detección y reconocimiento; aprendizaje y conductas adaptativas; interacción; robots blandos y reconfigurables; y robots industriales y de servicios. Nota de contenido: Swarm and multi-robotic systems -- Human-robot interaction -- Robotic learning and imitation -- Robot navigation, planning and safety -- Humanoid and bio-inspired robots -- Mobile robots and vehicles -- Robot testing and design -- Detection and recognition -- Learning and adaptive behaviours -- Interaction -- Soft and reconfigurable robots -- Service and industrial robots. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 18th Annual Conference on Towards Autonomous Robotics, TAROS 2017, held in Guildford, UK, in July 2017. The 43 revised full papers presented together with 13 short papers were carefully reviewed and selected from 66 submissions. The papers discuss robotics research drawn from a wide and diverse range of topics, such as swarm and multi-robotic systems; human-robot interaction; robotic learning and imitation; robot navigation, planning and safety; humanoid and bio-inspired robots; mobile robots and vehicles; robot testing and design; detection and recognition; learning and adaptive behaviours; interaction; soft and reconfigurable robots; and service and industrial robots. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Towards Autonomous Robotic Systems : 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings / [documento electrónico] / Gao, Yang, ; Fallah, Saber, ; Jin, Yaochu, ; Lekakou, Constantina,  . -  1 ed.  . - [s.l.] : Springer, 2017 . - XIII, 705 p. 400 ilustraciones. ISBN : 978-3-319-64107-2 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial  Computadoras  Propósitos especiales  Red de computadoras  Interfaces de usuario (sistemas informáticos)  La interacción persona-ordenador  Sistemas de propósito especial y basados ​​en aplicaciones  Redes de comunicación informática  Interfaces de usuario e interacción persona-computadora Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas de la 18.ª Conferencia Anual sobre la Robótica Autónoma, TAROS 2017, celebrada en Guildford, Reino Unido, en julio de 2017. Los 43 artículos completos revisados ​​presentados junto con 13 artículos breves fueron cuidadosamente revisados ​​y seleccionados entre 66 presentaciones. Los artículos analizan la investigación en robótica extraída de una amplia y diversa gama de temas, como sistemas enjambres y multirobóticos; interacción humano-robot; aprendizaje e imitación robótica; navegación, planificación y seguridad de robots; robots humanoides y bioinspirados; robots y vehículos móviles; pruebas y diseño de robots; detección y reconocimiento; aprendizaje y conductas adaptativas; interacción; robots blandos y reconfigurables; y robots industriales y de servicios. Nota de contenido: Swarm and multi-robotic systems -- Human-robot interaction -- Robotic learning and imitation -- Robot navigation, planning and safety -- Humanoid and bio-inspired robots -- Mobile robots and vehicles -- Robot testing and design -- Detection and recognition -- Learning and adaptive behaviours -- Interaction -- Soft and reconfigurable robots -- Service and industrial robots. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 18th Annual Conference on Towards Autonomous Robotics, TAROS 2017, held in Guildford, UK, in July 2017. The 43 revised full papers presented together with 13 short papers were carefully reviewed and selected from 66 submissions. The papers discuss robotics research drawn from a wide and diverse range of topics, such as swarm and multi-robotic systems; human-robot interaction; robotic learning and imitation; robot navigation, planning and safety; humanoid and bio-inspired robots; mobile robots and vehicles; robot testing and design; detection and recognition; learning and adaptive behaviours; interaction; soft and reconfigurable robots; and service and industrial robots. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

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