Koro¿ec peter (22 Ergebnisse)

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Taschenbuch. Zustand: Neu. Bioinspired Optimization Methods and Their Applications | 8th International Conference, BIOMA 2018, Paris, France, May 16-18, 2018, Proceedings | Peter Koro¿ec (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xiii | Englisch | 2018 | Springer | EAN 9783319916408 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios.The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis ¿ Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms ¿ Chapters 5-7.Part III: Implementation and application of Deep Statistical Comparison ¿ Chapter 8.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and applicationof DeepStatistical Comparison - Chapter 8. …

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and applicationof DeepStatistical Comparison - Chapter 8.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the thoroughly refereed revised selected papers of the 10 th International Conference on Bioinspired Optimization Models and Their Applications, BIOMA 2018, held in Paris, France, in May 2018.The 27 revised full papers were selected from 53 submissions and present papers in all aspects of bioinspired optimization research such as new algorithmic developments, high-impact applications, new research challenges, theoretical contributions, implementation issues, and experimental studies. 348 pp. Englisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Optimization of Home Care Visits Schedule by Genetic Algorithm.- New Techniques for Inferring L-systems Using Genetic Algorithm.- An Adaptive Metaheuristic for Unconstrained Multimodal Numerical Optimization.- Scrum Task Allocation Based on Particle Swarm Optimization.- Cooperative Model for Nature-Inspired Algorithms in Solving Real-World Optimization Problems.- Collaborative Agent Teams (CAT): from the Paradigm to Implementation Guidelines.- A Bio-inspired Approach for Collaborative Exploration with Mobile Battery Recharging in Swarm Robotics.- Constructive Metaheuristics for the Set Covering Problem.- Single and multiobjective evolutionary algorithms for clustering biomedical information with unknown number of clusters.- Evolutionary algorithms for scheduling of crude oil preheating process under linear fouling.- Hybrid weighted barebones exploiting particle swarm optimization algorithm for time series representation.- Data-driven Preference-based Deep Statistical Ranking for Comparing.- sMulti-Objective Optimization Algorithms.- Construction of heuristic for protein structure optimization using deep reinforcement learning.- Comparing Boundary Control Methods for Firefly Algorithm.- A New Binary Encoding Scheme in Genetic Algorithm for Solving the Capacitated Vehicle Routing Problem.- Ensemble and Fuzzy techniques applied to Imbalanced Traffic Congestion Datasets: a Comparative Study.- Multi-Objective Design of Time-Constrained Bike Routes using Bio-inspired Meta-Heuristics.- Ensemble of Kriging with Multiple Kernel Functions for Engineering Design Optimization.- Path Planning Optimization Method Based on Genetic Algorithm for Mapping Toxic Environment.- Tuning Multi-Objective Optimization Algorithms for the Integration and Testing Order Problem.- Surrogate-Assisted Particle Swarm with Local Search for Expensive Constrained Optimization.- Indicator-based versus Aspect-based Selection in Multi- and Many-objective Biochemical Optimization.- An Approach for Recovering Distributed Systems from Disasters.- Population Diversity Analysis for the Chaotic based Selection of Individuals in Differential Evolution.- Robust Design with Surrogate-Assisted Evolutionary Algorithm: Does it work .- How Distance based Parameter Adaptation Affects Population Diversity.- Collaborative Variable Neighborhood Search.¿Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 348 pp. Englisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and applicationof DeepStatistical Comparison - Chapter 8. 152 pp. Englisch.…

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and applicationof DeepStatistical Comparison - Chapter 8. 152 pp. Englisch.…