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Autor Zhang, Mengjie |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsqueda11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings / Shi, Yuhui ; Tan, Kay Chen ; Zhang, Mengjie ; Tang, Ke ; Li, Xiaodong ; Zhang, Qingfu ; Tan, Ying ; Middendorf, Martin ; Jin, Yaochu
TÃtulo : 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. Palabras clave: Ciencias de la Computación Inteligencia artificial Algoritmos Red de computadoras Simulación por ordenador TeorÃa de la Computación Modelos de Computación Redes de comunicación informática Modelado por computadora Clasificación: 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 artÃculos 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 caracterÃsticas 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.  . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 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.
Palabras clave: Ciencias de la Computación Inteligencia artificial Algoritmos Red de computadoras Simulación por ordenador TeorÃa de la Computación Modelos de Computación Redes de comunicación informática Modelado por computadora Clasificación: 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 artÃculos 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 caracterÃsticas 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.  . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Genetic Programming : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings Tipo de documento: documento electrónico Autores: Castelli, Mauro, ; Sekanina, Lukas, ; Zhang, Mengjie, ; Cagnoni, Stefano, ; GarcÃa-Sánchez, Pablo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XII, 323 p. 80 ilustraciones ISBN/ISSN/DL: 978-3-319-77553-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Algoritmos Unidades aritméticas y lógicas informáticas Inteligencia artificial Procesamiento de datos Estructuras de datos (Informática) TeorÃa de la información Estructuras aritméticas y lógicas MinerÃa de datos y descubrimiento de conocimientos Estructuras de datos y teorÃa de la información Clasificación: Resumen: Este libro constituye las actas arbitradas de la 21.ª Conferencia Europea sobre Programación Genética, EuroGP 2018, celebrada en Parma, Italia, en abril de 2018, junto con los eventos Evo* 2018, EvoCOP, EvoMUSART y EvoApplications. Los 11 artÃculos completos revisados ​​presentados junto con 8 artÃculos tipo póster fueron cuidadosamente revisados ​​y seleccionados entre 36 presentaciones. La amplia gama de temas de este volumen refleja el estado actual de la investigación en este campo. Por lo tanto, vemos temas y aplicaciones que incluyen análisis de caracterÃsticas importantes para la metabolómica, métodos semánticos, evolución de redes booleanas, generación de caracterÃsticas redundantes, conjuntos de modelos GP, diseño automático de representaciones gramaticales, GP y neuroevolución, aprendizaje por refuerzo visual, evolución de procesos profundos. redes neuronales, evolución de gráficos y programación en redes heterogéneas. Nota de contenido: Using GP Is NEAT: Evolving Compositional Pattern Production Functions -- Evolving the Topology of Large Scale Deep Neural Networks -- Evolving Graphs by Graph Programming -- Pruning Techniques for Mixed Ensembles of Genetic Programming Models -- Analyzing Feature Importance for Metabolomics Using Genetic Programming -- Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming -- On the Automatic Design of a Representation for Grammar-Based Genetic Programming -- Multi-Level Grammar Genetic Programming for Scheduling in Heterogeneous Networks -- Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom -- Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States -- A Multiple Expression Alignment Framework for Genetic Programming -- Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions -- A Comparative Study on Crossover in Cartesian Genetic Programming -- Evolving Better RNAfold Structure Prediction -- Geometric Crossover in Syntactic Space -- Investigating A Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling -- Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming -- Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web -- Genetic Programming Hyperheuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Genetic Programming : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings [documento electrónico] / Castelli, Mauro, ; Sekanina, Lukas, ; Zhang, Mengjie, ; Cagnoni, Stefano, ; GarcÃa-Sánchez, Pablo, . - 1 ed. . - [s.l.] : Springer, 2018 . - XII, 323 p. 80 ilustraciones.
ISBN : 978-3-319-77553-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Algoritmos Unidades aritméticas y lógicas informáticas Inteligencia artificial Procesamiento de datos Estructuras de datos (Informática) TeorÃa de la información Estructuras aritméticas y lógicas MinerÃa de datos y descubrimiento de conocimientos Estructuras de datos y teorÃa de la información Clasificación: Resumen: Este libro constituye las actas arbitradas de la 21.ª Conferencia Europea sobre Programación Genética, EuroGP 2018, celebrada en Parma, Italia, en abril de 2018, junto con los eventos Evo* 2018, EvoCOP, EvoMUSART y EvoApplications. Los 11 artÃculos completos revisados ​​presentados junto con 8 artÃculos tipo póster fueron cuidadosamente revisados ​​y seleccionados entre 36 presentaciones. La amplia gama de temas de este volumen refleja el estado actual de la investigación en este campo. Por lo tanto, vemos temas y aplicaciones que incluyen análisis de caracterÃsticas importantes para la metabolómica, métodos semánticos, evolución de redes booleanas, generación de caracterÃsticas redundantes, conjuntos de modelos GP, diseño automático de representaciones gramaticales, GP y neuroevolución, aprendizaje por refuerzo visual, evolución de procesos profundos. redes neuronales, evolución de gráficos y programación en redes heterogéneas. Nota de contenido: Using GP Is NEAT: Evolving Compositional Pattern Production Functions -- Evolving the Topology of Large Scale Deep Neural Networks -- Evolving Graphs by Graph Programming -- Pruning Techniques for Mixed Ensembles of Genetic Programming Models -- Analyzing Feature Importance for Metabolomics Using Genetic Programming -- Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming -- On the Automatic Design of a Representation for Grammar-Based Genetic Programming -- Multi-Level Grammar Genetic Programming for Scheduling in Heterogeneous Networks -- Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom -- Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States -- A Multiple Expression Alignment Framework for Genetic Programming -- Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions -- A Comparative Study on Crossover in Cartesian Genetic Programming -- Evolving Better RNAfold Structure Prediction -- Geometric Crossover in Syntactic Space -- Investigating A Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling -- Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming -- Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web -- Genetic Programming Hyperheuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Genetic Programming for Production Scheduling : An Evolutionary Learning Approach Tipo de documento: documento electrónico Autores: Zhang, Fangfang, ; Nguyen, Su, ; Mei, Yi, ; Zhang, Mengjie, Mención de edición: 1 ed. Editorial: Singapore [Malasya] : Springer Fecha de publicación: 2021 Número de páginas: XXXIII, 336 p. 154 ilustraciones, 105 ilustraciones en color. ISBN/ISSN/DL: 978-981-1648595-- Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Aprendizaje automático Sistemas expertos (Informática) IngenierÃa Industrial IngenierÃa de Producción La investigación de operaciones Sistemas basados ​​en el conocimiento IngenierÃa Industrial y de Producción Investigación de Operaciones y TeorÃa de la Decisión Clasificación: Resumen: Este libro presenta a los lectores un enfoque de aprendizaje evolutivo, especÃficamente la programación genética (GP), para la programación de la producción. El libro esta dividido en seis partes. En la Parte I, proporciona una introducción a la programación de la producción, los métodos de solución existentes y el enfoque GP para la programación de la producción. También se presentan las caracterÃsticas de los entornos de producción, la formulación de problemas, un marco de GP abstracto para la programación de la producción y los criterios de evaluación. La Parte II muestra varias formas en que se puede emplear GP para resolver problemas de programación de producción estática y sus conexiones con métodos convencionales de investigación operativa. A su vez, la Parte III muestra cómo diseñar algoritmos GP para problemas de programación de producción dinámica y describe técnicas avanzadas para mejorar el rendimiento de GP, incluida la selección de caracterÃsticas, modelado sustituto y operadores genéticos especializados. En la Parte IV, el libro aborda cómo utilizar la heurÃstica para abordar objetivos múltiples y potencialmente conflictivos en problemas de programación de producción, y presenta un enfoque multiobjetivo avanzado con técnicas de coevolución cooperativa o representaciones de múltiples árboles. La Parte V demuestra cómo utilizar técnicas de aprendizaje multitarea en el espacio hiperheurÃstico para la programación de producción. También muestra cómo las técnicas sustitutas y las estrategias de selección asistida de tareas pueden beneficiar el aprendizaje multitarea con GP para aprender heurÃsticas en el contexto de la programación de producción. La Parte VI completa el texto con una visión del futuro. Dado su alcance, el libro beneficia a cientÃficos, ingenieros, investigadores, profesionales, posgraduados y estudiantes universitarios en las áreas de aprendizaje automático, inteligencia artificial, computación evolutiva, investigación de operaciones e ingenierÃa industrial. Nota de contenido: Part I Introduction -- 1 Introduction -- 2 Preliminaries -- Part II Genetic Programming for Static Production Scheduling Problems -- 3 Learning Schedule Construction Heuristics -- 4 Learning Schedule Improvement Heuristics -- 5 Learning to Augment Operations Research Algorithms -- Part III Genetic Programming for Dynamic Production Scheduling Problems -- 6 Representations with Multi-tree and Cooperative Coevolution -- 7 Efficiency Improvement with Multi-ï¬delity Surrogates -- 8 Search Space Reduction with Feature Selection -- 9 Search Mechanism with Specialised Genetic Operators -- Part IV Genetic Programming for Multi-objective Production Scheduling Problems -- 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems -- 11 Cooperative Coevolutionary for Multi-objective Production Scheduling Problems -- 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling -- Part V Multitask Genetic Programming for Production Scheduling Problems -- 13 Multitask Learning in Hyper-heuristic Domain with Dynamic Production Scheduling -- 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling -- 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics -- Part VI Conclusions and Prospects -- 16 Conclusions and Prospects. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Genetic Programming for Production Scheduling : An Evolutionary Learning Approach [documento electrónico] / Zhang, Fangfang, ; Nguyen, Su, ; Mei, Yi, ; Zhang, Mengjie, . - 1 ed. . - Singapore [Malasya] : Springer, 2021 . - XXXIII, 336 p. 154 ilustraciones, 105 ilustraciones en color.
ISBN : 978-981-1648595--
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Aprendizaje automático Sistemas expertos (Informática) IngenierÃa Industrial IngenierÃa de Producción La investigación de operaciones Sistemas basados ​​en el conocimiento IngenierÃa Industrial y de Producción Investigación de Operaciones y TeorÃa de la Decisión Clasificación: Resumen: Este libro presenta a los lectores un enfoque de aprendizaje evolutivo, especÃficamente la programación genética (GP), para la programación de la producción. El libro esta dividido en seis partes. En la Parte I, proporciona una introducción a la programación de la producción, los métodos de solución existentes y el enfoque GP para la programación de la producción. También se presentan las caracterÃsticas de los entornos de producción, la formulación de problemas, un marco de GP abstracto para la programación de la producción y los criterios de evaluación. La Parte II muestra varias formas en que se puede emplear GP para resolver problemas de programación de producción estática y sus conexiones con métodos convencionales de investigación operativa. A su vez, la Parte III muestra cómo diseñar algoritmos GP para problemas de programación de producción dinámica y describe técnicas avanzadas para mejorar el rendimiento de GP, incluida la selección de caracterÃsticas, modelado sustituto y operadores genéticos especializados. En la Parte IV, el libro aborda cómo utilizar la heurÃstica para abordar objetivos múltiples y potencialmente conflictivos en problemas de programación de producción, y presenta un enfoque multiobjetivo avanzado con técnicas de coevolución cooperativa o representaciones de múltiples árboles. La Parte V demuestra cómo utilizar técnicas de aprendizaje multitarea en el espacio hiperheurÃstico para la programación de producción. También muestra cómo las técnicas sustitutas y las estrategias de selección asistida de tareas pueden beneficiar el aprendizaje multitarea con GP para aprender heurÃsticas en el contexto de la programación de producción. La Parte VI completa el texto con una visión del futuro. Dado su alcance, el libro beneficia a cientÃficos, ingenieros, investigadores, profesionales, posgraduados y estudiantes universitarios en las áreas de aprendizaje automático, inteligencia artificial, computación evolutiva, investigación de operaciones e ingenierÃa industrial. Nota de contenido: Part I Introduction -- 1 Introduction -- 2 Preliminaries -- Part II Genetic Programming for Static Production Scheduling Problems -- 3 Learning Schedule Construction Heuristics -- 4 Learning Schedule Improvement Heuristics -- 5 Learning to Augment Operations Research Algorithms -- Part III Genetic Programming for Dynamic Production Scheduling Problems -- 6 Representations with Multi-tree and Cooperative Coevolution -- 7 Efficiency Improvement with Multi-ï¬delity Surrogates -- 8 Search Space Reduction with Feature Selection -- 9 Search Mechanism with Specialised Genetic Operators -- Part IV Genetic Programming for Multi-objective Production Scheduling Problems -- 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems -- 11 Cooperative Coevolutionary for Multi-objective Production Scheduling Problems -- 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling -- Part V Multitask Genetic Programming for Production Scheduling Problems -- 13 Multitask Learning in Hyper-heuristic Domain with Dynamic Production Scheduling -- 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling -- 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics -- Part VI Conclusions and Prospects -- 16 Conclusions and Prospects. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
