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Autor Trautmann, Heike |
Documentos disponibles escritos por este autor (4)
Crear una solicitud de compra Refinar búsquedaApplications in Statistical Computing / Bauer, Nadja ; Ickstadt, Katja ; Lübke, Karsten ; Szepannek, Gero ; Trautmann, Heike ; Vichi, Maurizio
TÃtulo : Applications in Statistical Computing : From Music Data Analysis to Industrial Quality Improvement Tipo de documento: documento electrónico Autores: Bauer, Nadja, ; Ickstadt, Katja, ; Lübke, Karsten, ; Szepannek, Gero, ; Trautmann, Heike, ; Vichi, Maurizio, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XI, 340 p. 83 ilustraciones, 48 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-25147-5 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: Procesamiento de datos EstadÃsticas Informática Estadistica matematica Música La investigación de operaciones EstadÃstica y Computación MinerÃa de datos y descubrimiento de conocimientos EstadÃsticas aplicadas Probabilidad y EstadÃstica en Informática Matemáticas en la música Investigación de Operaciones y TeorÃa de la Decisión Clasificación: 519.5 Resumen: Este volumen presenta una selección de trabajos de investigación sobre diversos temas en la interfaz de la estadÃstica y la informática. Se pone énfasis en las aplicaciones prácticas de métodos estadÃsticos en diversas disciplinas, utilizando el aprendizaje automático y otros métodos computacionales. El libro cubre campos de investigación que incluyen el diseño de experimentos, estadÃstica computacional, análisis de datos musicales, control estadÃstico de procesos, biometrÃa, ingenierÃa industrial y econometrÃa. El volumen, que reúne contribuciones innovadoras, de alta calidad y cientÃficamente relevantes, se publicó en honor de Claus Weihs, profesor de EstadÃstica Computacional en la Universidad TU Dortmund, con motivo de su 66 cumpleaños. Nota de contenido: Part I Methodological Developments in Data Science.-Aviation Data Analysis by Linear Programming in Airline Network Revenue Management -- Bayesian Reduced Rank Regression for Classification -- Modelling and classification of GC/IMS breath gas measurements for lozenges of different flavours -- The Cosine Depth Distribution Classifier for Directional Data -- A Nonconformity Ratio Based Desirability Function for Capability Assessment -- Part II Computational Statistics -- Heteroscedastic Discriminant Analysis using R -- Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco -- Part III Perspectives on Statistics and Data Science -- A Note on Artificial Intelligence and Statistics -- Statistical Computing and Data Science in Introductory Statistics -- Approaching Ethical Guidelines for Data Scientists -- Part IV Statistics in Econometric Applications -- Dating Lower Turning Points of Business Cycles – a Multivariate Linear Discriminant Analysis for Germany 1984 to 2009 -- Partial Orderings of Default Predictions -- Improving GMM efficiency in dynamic models for panel data with mean stationarity -- Part V Statistics in Industrial Applications -- Economically designed Bayesian np control charts using dual sample sizes for long-run processes -- Statistical analysis of the lifetime of diamond impregnated tools for core drilling of concrete -- Detection of anomalous sequences in crack data of a bridge monitoring -- Optimal Semi-Split-Plot Designs with R -- Continuous process monitoring through ensemble based anomaly detection -- Part VI Statistics in Music Applications -- Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music -- The Psychological Foundations of Classification. Tipo de medio : Computadora Summary : This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Applications in Statistical Computing : From Music Data Analysis to Industrial Quality Improvement [documento electrónico] / Bauer, Nadja, ; Ickstadt, Katja, ; Lübke, Karsten, ; Szepannek, Gero, ; Trautmann, Heike, ; Vichi, Maurizio, . - 1 ed. . - [s.l.] : Springer, 2019 . - XI, 340 p. 83 ilustraciones, 48 ilustraciones en color.
ISBN : 978-3-030-25147-5
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: Procesamiento de datos EstadÃsticas Informática Estadistica matematica Música La investigación de operaciones EstadÃstica y Computación MinerÃa de datos y descubrimiento de conocimientos EstadÃsticas aplicadas Probabilidad y EstadÃstica en Informática Matemáticas en la música Investigación de Operaciones y TeorÃa de la Decisión Clasificación: 519.5 Resumen: Este volumen presenta una selección de trabajos de investigación sobre diversos temas en la interfaz de la estadÃstica y la informática. Se pone énfasis en las aplicaciones prácticas de métodos estadÃsticos en diversas disciplinas, utilizando el aprendizaje automático y otros métodos computacionales. El libro cubre campos de investigación que incluyen el diseño de experimentos, estadÃstica computacional, análisis de datos musicales, control estadÃstico de procesos, biometrÃa, ingenierÃa industrial y econometrÃa. El volumen, que reúne contribuciones innovadoras, de alta calidad y cientÃficamente relevantes, se publicó en honor de Claus Weihs, profesor de EstadÃstica Computacional en la Universidad TU Dortmund, con motivo de su 66 cumpleaños. Nota de contenido: Part I Methodological Developments in Data Science.-Aviation Data Analysis by Linear Programming in Airline Network Revenue Management -- Bayesian Reduced Rank Regression for Classification -- Modelling and classification of GC/IMS breath gas measurements for lozenges of different flavours -- The Cosine Depth Distribution Classifier for Directional Data -- A Nonconformity Ratio Based Desirability Function for Capability Assessment -- Part II Computational Statistics -- Heteroscedastic Discriminant Analysis using R -- Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco -- Part III Perspectives on Statistics and Data Science -- A Note on Artificial Intelligence and Statistics -- Statistical Computing and Data Science in Introductory Statistics -- Approaching Ethical Guidelines for Data Scientists -- Part IV Statistics in Econometric Applications -- Dating Lower Turning Points of Business Cycles – a Multivariate Linear Discriminant Analysis for Germany 1984 to 2009 -- Partial Orderings of Default Predictions -- Improving GMM efficiency in dynamic models for panel data with mean stationarity -- Part V Statistics in Industrial Applications -- Economically designed Bayesian np control charts using dual sample sizes for long-run processes -- Statistical analysis of the lifetime of diamond impregnated tools for core drilling of concrete -- Detection of anomalous sequences in crack data of a bridge monitoring -- Optimal Semi-Split-Plot Designs with R -- Continuous process monitoring through ensemble based anomaly detection -- Part VI Statistics in Music Applications -- Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music -- The Psychological Foundations of Classification. Tipo de medio : Computadora Summary : This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : 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 artÃculos 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 hÃbridos; 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 artÃculos 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 hÃbridos; 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 [...]
TÃtulo : Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I Tipo de documento: documento electrónico Autores: Bäck, Thomas, ; Preuss, Mike, ; Deutz, André, ; Wang, Hao, ; Doerr, Carola, ; Emmerich, Michael, ; Trautmann, Heike, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXIX, 735 p. 261 ilustraciones, 169 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-58112-1 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 Mathematical statistics Redes de comunicación informática Probabilidad y EstadÃstica en Informática Matemáticas discretas en informática Matemáticas de la Computación Red informática Informática Matemáticas discretas Matemáticas TeorÃa de la Computación Clasificación: 40.151 Resumen: Este conjunto de dos volúmenes LNCS 12269 y LNCS 12270 constituye las actas arbitradas de la 16.ª Conferencia Internacional sobre Resolución de Problemas Paralelos a partir de la Naturaleza, PPSN 2020, celebrada en Leiden, PaÃses Bajos, en septiembre de 2020. Los 99 artÃculos completos revisados ​​fueron cuidadosamente revisados ​​y seleccionados. de 268 presentaciones. Los temas cubren temas clásicos como la selección y configuración de algoritmos automatizados; Optimización asistida por bayesiano y sustituto; evaluación comparativa y medidas de desempeño; optimización combinatoria; conexión entre la optimización inspirada en la naturaleza y la inteligencia artificial; algoritmos genéticos y evolutivos; programación genética; análisis del paisaje; optimización multiobjetivo; aplicaciones del mundo real; aprendizaje reforzado; y aspectos teóricos de la optimización inspirada en la naturaleza. Nota de contenido: Automated Algorithm Selection and Configuration -- Bayesian- and Surrogate-Assisted Optimization -- Benchmarking and Performance Measures -- Combinatorial Optimization -- Connection Between Nature-Inspired Optimization and Artificial Intelligence -- Genetic and Evolutionary Algorithms. Tipo de medio : Computadora Summary : This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I [documento electrónico] / Bäck, Thomas, ; Preuss, Mike, ; Deutz, André, ; Wang, Hao, ; Doerr, Carola, ; Emmerich, Michael, ; Trautmann, Heike, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXIX, 735 p. 261 ilustraciones, 169 ilustraciones en color.
ISBN : 978-3-030-58112-1
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 Mathematical statistics Redes de comunicación informática Probabilidad y EstadÃstica en Informática Matemáticas discretas en informática Matemáticas de la Computación Red informática Informática Matemáticas discretas Matemáticas TeorÃa de la Computación Clasificación: 40.151 Resumen: Este conjunto de dos volúmenes LNCS 12269 y LNCS 12270 constituye las actas arbitradas de la 16.ª Conferencia Internacional sobre Resolución de Problemas Paralelos a partir de la Naturaleza, PPSN 2020, celebrada en Leiden, PaÃses Bajos, en septiembre de 2020. Los 99 artÃculos completos revisados ​​fueron cuidadosamente revisados ​​y seleccionados. de 268 presentaciones. Los temas cubren temas clásicos como la selección y configuración de algoritmos automatizados; Optimización asistida por bayesiano y sustituto; evaluación comparativa y medidas de desempeño; optimización combinatoria; conexión entre la optimización inspirada en la naturaleza y la inteligencia artificial; algoritmos genéticos y evolutivos; programación genética; análisis del paisaje; optimización multiobjetivo; aplicaciones del mundo real; aprendizaje reforzado; y aspectos teóricos de la optimización inspirada en la naturaleza. Nota de contenido: Automated Algorithm Selection and Configuration -- Bayesian- and Surrogate-Assisted Optimization -- Benchmarking and Performance Measures -- Combinatorial Optimization -- Connection Between Nature-Inspired Optimization and Artificial Intelligence -- Genetic and Evolutionary Algorithms. Tipo de medio : Computadora Summary : This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II Tipo de documento: documento electrónico Autores: Bäck, Thomas, ; Preuss, Mike, ; Deutz, André, ; Wang, Hao, ; Doerr, Carola, ; Emmerich, Michael, ; Trautmann, Heike, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XXIX, 717 p. 318 ilustraciones, 146 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-58115-2 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: TeorÃa de la Computación Probabilidad y EstadÃstica en Informática IngenierÃa de software Matemáticas discretas en informática Inteligencia artificial Mathematical statistics Matemáticas discretas Matemáticas Informática Matemáticas de la Computación Clasificación: 40.151 Resumen: Este conjunto de dos volúmenes LNCS 12269 y LNCS 12270 constituye las actas arbitradas de la 16.ª Conferencia Internacional sobre Resolución de Problemas Paralelos a partir de la Naturaleza, PPSN 2020, celebrada en Leiden, PaÃses Bajos, en septiembre de 2020. Los 99 artÃculos completos revisados ​​fueron cuidadosamente revisados ​​y seleccionados. de 268 presentaciones. Los temas cubren temas clásicos como la selección y configuración de algoritmos automatizados; Optimización asistida por bayesiano y sustituto; evaluación comparativa y medidas de desempeño; optimización combinatoria; conexión entre la optimización inspirada en la naturaleza y la inteligencia artificial; algoritmos genéticos y evolutivos; programación genética; análisis del paisaje; optimización multiobjetivo; aplicaciones del mundo real; aprendizaje reforzado; y aspectos teóricos de la optimización inspirada en la naturaleza. Nota de contenido: Genetic Programming -- Landscape Analysis -- Multiobjective Optimization -- Real-World Applications -- Reinforcement Learning -- Theoretical Aspects of Nature-Inspired Optimization. . Tipo de medio : Computadora Summary : This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II [documento electrónico] / Bäck, Thomas, ; Preuss, Mike, ; Deutz, André, ; Wang, Hao, ; Doerr, Carola, ; Emmerich, Michael, ; Trautmann, Heike, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXIX, 717 p. 318 ilustraciones, 146 ilustraciones en color.
ISBN : 978-3-030-58115-2
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: TeorÃa de la Computación Probabilidad y EstadÃstica en Informática IngenierÃa de software Matemáticas discretas en informática Inteligencia artificial Mathematical statistics Matemáticas discretas Matemáticas Informática Matemáticas de la Computación Clasificación: 40.151 Resumen: Este conjunto de dos volúmenes LNCS 12269 y LNCS 12270 constituye las actas arbitradas de la 16.ª Conferencia Internacional sobre Resolución de Problemas Paralelos a partir de la Naturaleza, PPSN 2020, celebrada en Leiden, PaÃses Bajos, en septiembre de 2020. Los 99 artÃculos completos revisados ​​fueron cuidadosamente revisados ​​y seleccionados. de 268 presentaciones. Los temas cubren temas clásicos como la selección y configuración de algoritmos automatizados; Optimización asistida por bayesiano y sustituto; evaluación comparativa y medidas de desempeño; optimización combinatoria; conexión entre la optimización inspirada en la naturaleza y la inteligencia artificial; algoritmos genéticos y evolutivos; programación genética; análisis del paisaje; optimización multiobjetivo; aplicaciones del mundo real; aprendizaje reforzado; y aspectos teóricos de la optimización inspirada en la naturaleza. Nota de contenido: Genetic Programming -- Landscape Analysis -- Multiobjective Optimization -- Real-World Applications -- Reinforcement Learning -- Theoretical Aspects of Nature-Inspired Optimization. . Tipo de medio : Computadora Summary : This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
