Download or read book Deep Statistical Comparison for Meta heuristic Stochastic Optimization Algorithms written by Tome Eftimov and published by Springer Nature. This book was released on 2022-06-11 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning Optimization and Big Data written by Giuseppe Nicosia and published by Springer. This book was released on 2017-12-19 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Download or read book Bioinspired Optimization Methods and Their Applications written by Peter Korošec and published by Springer. This book was released on 2018-05-11 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed revised selected papers of the 10th 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.

Download or read book Evolutionary Multi Criterion Optimization written by Hisao Ishibuchi and published by Springer Nature. This book was released on 2021-03-24 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.

Download or read book Modelling and Development of Intelligent Systems written by Dana Simian and published by Springer Nature. This book was released on 2021-02-12 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 7th International Conference on Modelling and Development of Intelligent Systems, MDIS 2020, held in Sibiu, Romania, in October 2020. Due to the COVID-19 pandemic the conference was held online. The 25 revised full papers presented in the volume were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on ​evolutionary computing; intelligent systems for decision support; machine learning; mathematical models for development of intelligent systems; modelling and optimization of dynamic systems; ontology engineering.

Download or read book Heuristics for Optimization and Learning written by Farouk Yalaoui and published by Springer Nature. This book was released on 2020-12-15 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

Download or read book Computational Intelligence Applied to Inverse Problems in Radiative Transfer written by Antônio José da Silva Neto and published by Springer Nature. This book was released on 2024-01-13 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies. From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems. While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding. This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas.

Download or read book Stochastic Global Optimization written by Anatoly Zhigljavsky and published by Springer. This book was released on 2010-11-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book’s features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.

Download or read book Internet of Things Smart Spaces and Next Generation Networks and Systems written by Yevgeni Koucheryavy and published by Springer Nature. This book was released on 2023-04-19 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 22nd International Conference on Internet of Things, Smart Spaces, and Next Generation Networks and Systems, NEW2AN 2022, held in Tashkent, Uzbekistan, in December 2022. The 58 regular papers presented in this volume were carefully reviewed and selected from 282 submissions. The papers of NEW2AN address various aspects of next-generation data networks, while special attention is given to advanced wireless networking and applications. In particular, the authors have demonstrated novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and machine learning. It is also worth mentioning the rich coverage of the Internet of Things, optics, signal processing, as well as digital economy and business aspects.

Download or read book Comparisons Among Stochastic Optimization Algorithms written by Debao Chen and published by . This book was released on 1997 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Applications of Evolutionary Computation written by João Correia and published by Springer Nature. This book was released on 2023-04-08 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design. The EvoApplications 2023 presents papers on the different areas: Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications, Applications of Bio-inspired Techniques on Social Networks, Evolutionary Computation in Edge, Fog, and Cloud Computing, Evolutionary Computation in Image Analysis, Signal Processing, and Pattern Recognition and others.

Download or read book Stochastic Optimization written by Johannes Schneider and published by Springer. This book was released on 2010-11-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

Download or read book Statistical Aspects of Stochastic Optimization Algorithms written by Jianchang Hu and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many statistical and machine learning applications, obtaining the estimators or classifiers relies on solving certain optimization problems. However, finding the exact solution, such as the maximum likelihood estimator, is a computational challenge in general. Thus, people usually resort to stochastic algorithms to give approximate solutions. In this dissertation, we consider two stochastic optimization algorithms, the quantum annealing for combinatorial problems and stochastic gradient descent algorithm for continuous ones. For the quantum annealing procedure, we propose two data augmentation algorithms to sample from the approximate distribution. One shows potential speed-up over the existing algorithm, and the other reveals more insights of the approximate system. For the stochastic gradient descent algorithm, we consider its utilization in the recently developed deep learning technology where the objective function is typically non-convex. We study the relationship between convergence properties of the algorithm and the local curvature of a minimum. We also link the generalization ability and local curvature of a global minimum from a statistical perspective. Although these optimization tools are proposed from an algorithmic perspective, they are stochastic in nature. Hence, investigations from a statistical point of view can provide interesting findings and help better understand and control the optimization process.

Download or read book Statistical Learning Theory and Stochastic Optimization written by Olivier Catoni and published by Springer. This book was released on 2004-08-30 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.

Download or read book Handbook of Whale Optimization Algorithm written by Seyedali Mirjalili and published by Elsevier. This book was released on 2023-11-24 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book. Provides in-depth analysis of equations, mathematical models and mechanisms of the Whale Optimization Algorithm Proposes different variants of the Whale Optimization Algorithm to solve binary, multiobjective, noisy, dynamic and combinatorial optimization problems Demonstrates how to design, develop and test different hybrids of Whale Optimization Algorithm Introduces several application areas of the Whale Optimization Algorithm, focusing on sustainability Includes source code from applications and algorithms that is available online

Download or read book Metaheuristic and Machine Learning Optimization Strategies for Complex Systems written by R., Thanigaivelan and published by IGI Global. This book was released on 2024-07-17 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.

Download or read book Stochastic Optimization written by Ioannis Dritsas and published by BoD – Books on Demand. This book was released on 2011-02-28 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields.