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| evolutionary optimization algorithms dan simon: Evolutionary Optimization Algorithms Dan Simon, 2013-06-13 A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. |
| evolutionary optimization algorithms dan simon: Evolutionary Computation with Biogeography-based Optimization Haiping Ma, Dan Simon, 2017-02-06 Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems. |
| evolutionary optimization algorithms dan simon: Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy Deb, 2001-07-05 Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive coverage of this growing area of research Carefully introduces each algorithm with examples and in-depth discussion Includes many applications to real-world problems, including engineering design and scheduling Includes discussion of advanced topics and future research Can be used as a course text or for self-study Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. |
| evolutionary optimization algorithms dan simon: Optimal State Estimation Dan Simon, 2006-06-19 A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries. |
| evolutionary optimization algorithms dan simon: Evolutionary Algorithms for Solving Multi-Objective Problems Carlos Coello Coello, Gary B. Lamont, David A. van Veldhuizen, 2007-09-18 This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions. |
| evolutionary optimization algorithms dan simon: Introduction to Genetic Algorithms S.N. Sivanandam, S. N. Deepa, 2007-10-24 Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed. |
| evolutionary optimization algorithms dan simon: Evolutionary Computation with Biogeography-based Optimization Haiping Ma, Dan Simon, 2017-01-18 Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems. |
| evolutionary optimization algorithms dan simon: The Money Hackers Daniel P. Simon, 2020-04-14 Businesses, investors, and consumers are grappling with the seismic daily changes technology has brought to the banking and finance industry. The Money Hackers is the story of fintech’s major players and explores how these disruptions are transforming even money itself. Whether you’ve heard of fintech or not, it’s already changing your life. Have you ever “Venmoed” someone? Do you think of investing in Bitcoin--even though you can’t quite explain what it is? If you’ve deposited a check using your iPhone, that’s fintech. If you’ve gone to a bank branch and found it’s been closed for good, odds are that’s because of fintech too. This book focuses on some of fintech’s most powerful disruptors--a ragtag collection of financial outsiders and savants--and uses their incredible stories to explain not just how the technology works, but how the Silicon Valley thinking behind the technology, ideas like friction, hedonic adaptation, democratization, and disintermediation, is having a drastic effect on the entire banking and finance industry. Upon reading The Money Hackers, you will: Feel empowered with the knowledge needed to spot the opportunities the next wave of fintech disruptions will bring. Understand the critical pain points that fintech is resolving, through a profile of the major finsurgents behind the disruption. Topic areas include Friction (featuring founders of Venmo), Aggregate and Automate (featuring Adam Dell, founder of Open Table and brother of Michael Dell), and Rise of the Machines (featuring Jon Stein, founder of robo-advisor Betterment). Learn about some of the larger-than-life characters behind the fintech movement. The Money Hackers tells the fascinating story of fintech--how it began, and where it is likely taking us. |
| evolutionary optimization algorithms dan simon: Applied Evolutionary Algorithms for Engineers using Python Leonardo Azevedo Scardua, 2021-06-14 Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems. The presentation of the theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems. Cases of successful application of evolutionary algorithms to real-world like optimization problems are presented, together with source code that allows the reader to gain insight into the idiosyncrasies of the practical application of evolutionary algorithms. Key Features Includes detailed descriptions of evolutionary algorithm paradigms Provides didactic implementations of the algorithms in Python, a programming language that has been widely adopted by the AI community Discusses the application of evolutionary algorithms to real-world optimization problems Presents successful cases of the application of evolutionary algorithms to complex optimization problems, with auxiliary source code. |
| evolutionary optimization algorithms dan simon: Multi-objective Evolutionary Algorithms Sanaz Mostaghim, 2005 |
| evolutionary optimization algorithms dan simon: Nature-Inspired Optimization Algorithms Xin-She Yang, 2014-02-17 Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. - Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature - Provides a theoretical understanding as well as practical implementation hints - Provides a step-by-step introduction to each algorithm |
| evolutionary optimization algorithms dan simon: Evolutionary Computation Techniques: A Comparative Perspective Erik Cuevas, Valentín Osuna, Diego Oliva, 2016-12-28 This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods. |
| evolutionary optimization algorithms dan simon: Artificial Intelligence and Games Georgios N. Yannakakis, Julian Togelius, 2025-07-04 This book covers artificial intelligence methods applied to games, both in research and game development. It is aimed at graduate students, researchers, game developers, and readers with a technical background interested in the intersection of AI and games. The book covers a range of AI methods, from traditional search, planning, and optimization, to modern machine learning methods, including diffusion models and large language models. It discusses applications to playing games, generating content, and modeling players, including use cases such as level generation, game testing, intelligent non-player characters, player retention, player experience analysis, and game adaptation. It also covers the use of games, including video games, to test and benchmark AI algorithms. The book is informed by decades of research and practice in the field and combines insights into game design with deep technical knowledge from the authors, who have pioneered many of the methods and approaches used in the field. This second edition of the 2018 textbook captures significant developments in AI and gaming over the past 7 years, incorporating advancements in computer vision, reinforcement learning, deep learning, and the emergence of transformer-based large language models and generative AI. The book has been reorganized to provide an updated overview of AI in games, with separate sections dedicated to AI’s core uses in playing and generating games, and modeling their players, along with a new chapter on ethical considerations. Aimed at readers with foundational AI knowledge, the book primarily targets three audiences: graduate or advanced undergraduate students pursuing careers in game AI, AI researchers and educators seeking teaching resources, and game programmers interested in creative AI applications. The text is complemented by a website featuring exercises, lecture slides, and additional educational materials suitable for undergraduate and graduate courses. |
| evolutionary optimization algorithms dan simon: Advanced Classification Techniques for Healthcare Analysis Chakraborty, Chinmay, 2019-02-22 Medical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals’ success rates and treatment options become limited and fall to disastrous levels. Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care. |
| evolutionary optimization algorithms dan simon: The Smartness Mandate Orit Halpern, Robert Mitchell, 2023-01-10 Over the last half century, smartness—the drive for ubiquitous computing—has become a mandate: a new mode of managing and governing politics, economics, and the environment. Smart phones. Smart cars. Smart homes. Smart cities. The imperative to make our world ever smarter in the face of increasingly complex challenges raises several questions: What is this smartness mandate? How has it emerged, and what does it say about our evolving way of understanding—and managing—reality? How have we come to see the planet and its denizens first and foremost as data-collecting instruments? In The Smartness Mandate, Orit Halpern and Robert Mitchell radically suggest that smartness is not primarily a technology, but rather an epistemology. Through this lens, they offer a critical exploration of the practices, technologies, and subjects that such an understanding relies upon—above all, artificial intelligence and machine learning. The authors approach these not simply as techniques for solving problems of calculations, but rather as modes of managing life (human and other) in terms of neo-Darwinian evolution, distributed intelligences, and resilience, all of which have serious implications for society, politics, and the environment. The smartness mandate constitutes a new form of planetary governance, and Halpern and Mitchell aim to map the logic of this seemingly inexorable and now naturalized demand to compute, to illuminate the genealogy of how we arrived here and to point to alternative imaginaries of the possibilities and potentials of smart technologies and infrastructures. |
| evolutionary optimization algorithms dan simon: A Mathematical Theory of Design: Foundations, Algorithms and Applications D. Braha, O. Maimon, 2013-04-17 Formal Design Theory (PDT) is a mathematical theory of design. The main goal of PDT is to develop a domain independent core model of the design process. The book focuses the reader's attention on the process by which ideas originate and are developed into workable products. In developing PDT, we have been striving toward what has been expressed by the distinguished scholar Simon (1969): that the science of design is possible and some day we will be able to talk in terms of well-established theories and practices. The book is divided into five interrelated parts. The conceptual approach is presented first (Part I); followed by the theoretical foundations of PDT (Part II), and from which the algorithmic and pragmatic implications are deduced (Part III). Finally, detailed case-studies illustrate the theory and the methods of the design process (Part IV), and additional practical considerations are evaluated (Part V). The generic nature of the concepts, theory and methods are validated by examples from a variety of disciplines. FDT explores issues such as: algebraic representation of design artifacts, idealized design process cycle, and computational analysis and measurement of design process complexity and quality. FDT's axioms convey the assumptions of the theory about the nature of artifacts, and potential modifications of the artifacts in achieving desired goals or functionality. By being able to state these axioms explicitly, it is possible to derive theorems and corollaries, as well as to develop specific analytical and constructive methodologies. |
| evolutionary optimization algorithms dan simon: Automated Machine Learning and Meta-Learning for Multimedia Wenwu Zhu, Xin Wang, 2022-01-01 This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book. |
| evolutionary optimization algorithms dan simon: Research Anthology on Artificial Intelligence Applications in Security Management Association, Information Resources, 2020-11-27 As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research. |
| evolutionary optimization algorithms dan simon: Green, Pervasive, and Cloud Computing Shijian Li, 2019-03-14 This book constitutes the proceedings of the 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018, held in Hangzhou, China, in May 2018. The 35 full papers included in this volume were carefully reviewed and selected from 101 initial submissions. They are organized in the following topical sections: network security, and privacy-preserving; pervasive sensing and analysis; cloud computing, mobile computing, and crowd sensing; social and urban computing; parallel and distributed systems, optimization; pervasive applications; and data mining and knowledge mining. |
| evolutionary optimization algorithms dan simon: Intelligent Information Processing and Web Mining Mieczyslaw A. Klopotek, Slawomir T. Wierzchon, Krzysztof Trojanowski, 2006-05-28 The international conference Intelligent Information Processing and Web Mining IIS:IIPWM’05, organized in Gda?sk-Sobieszewo on 13–16th June, 2005, was a continuation of a long tradition of conferences on applications of Arti?cial Intelligence (AI) in Information Systems (IS), organized by the Institute of Computer Science of Polish Academy of Sciences in cooperation with other scienti?c and business institutions. The Institute itself is deeply engaged in research both in AI and IS and many scientists view it as a leading institution both in fundamental and - plied research in these areas in Poland. The originators of this conference series, Prof. M. D?browski and Dr. M. Michalewicz had in 1992 a long-term goal of bringing together scientists and industry of di?erent braches from Poland and abroad to achieve a creative synthesis. One can say that their dream has come to reality. Scientists from ?ve continents made their subm- sions to this conference. A brief look at the a?liations makes international cooperation visible. The research papers have either a motivation in c- crete applications or are o?-springs of some practical requests. This volume presents the best papers carefully chosen from a large set of submissions (about 45%). At this point we would like to express our thanks to the m- bers of Programme Committee for their excellent job. Also we are thankful to the organizers of the special sessions accompanying this conference: Jan Komorowski, Adam Przepiórkowski, Zbigniew W. |
| evolutionary optimization algorithms dan simon: Harmony Search Algorithm Joong Hoon Kim, Zong Woo Geem, 2015-08-08 The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community. This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications. The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques. This book offers a valuable snapshot of the current status of the Harmony Search Algorithm and related techniques, and will be a useful reference for practising researchers and advanced students in computer science and engineering. |
| evolutionary optimization algorithms dan simon: Animal Communication Theory Ulrich Stegmann, 2013-05-02 A valuable overview and analysis of foundational concepts in animal behaviour studies, including information, meaning, communication, signals and cues. Its comprehensive introduction and numerous illustrations will make it accessible to students and researchers from a wide variety of academic backgrounds, ranging from ethology and evolutionary biology to philosophy of mind. |
| evolutionary optimization algorithms dan simon: Applied Stochastic Differential Equations Simo Särkkä, Arno Solin, 2019-05-02 With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice. |
| evolutionary optimization algorithms dan simon: Automated Machine Learning in Action Qingquan Song, Haifeng Jin, Xia Hu, 2022-06-07 Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner. In Automated Machine Learning in Action you will learn how to: Improve a machine learning model by automatically tuning its hyperparameters Pick the optimal components for creating and improving your pipelines Use AutoML toolkits such as AutoKeras and KerasTuner Design and implement search algorithms to find the best component for your ML task Accelerate the AutoML process with data-parallel, model pretraining, and other techniques Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you’re new to ML, you’ll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. By accelerating and standardizing work throughout the ML pipeline, AutoML frees up valuable data scientist time and enables less experienced users to apply machine learning effectively. About the book Automated Machine Learning in Action shows you how to save time and get better results using AutoML. As you go, you’ll learn how each component of an ML pipeline can be automated with AutoKeras and KerasTuner. The book is packed with techniques for automating classification, regression, data augmentation, and more. The payoff: Your ML systems will be able to tune themselves with little manual work. What's inside Automatically tune model hyperparameters Pick the optimal pipeline components Select appropriate models and features Learn different search algorithms and acceleration strategies About the reader For ML novices building their first pipelines and experienced ML engineers looking to automate tasks. About the author Drs. Qingquan Song, Haifeng Jin, and Xia “Ben” Hu are the creators of the AutoKeras automated deep learning library. Table of Contents PART 1 FUNDAMENTALS OF AUTOML 1 From machine learning to automated machine learning 2 The end-to-end pipeline of an ML project 3 Deep learning in a nutshell PART 2 AUTOML IN PRACTICE 4 Automated generation of end-to-end ML solutions 5 Customizing the search space by creating AutoML pipelines 6 AutoML with a fully customized search space PART 3 ADVANCED TOPICS IN AUTOML 7 Customizing the search method of AutoML 8 Scaling up AutoML 9 Wrapping up |
| evolutionary optimization algorithms dan simon: The Sciences of the Artificial, third edition Herbert A. Simon, 1996-09-26 Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial intelligence adds a chapter that sorts out the current themes and tools—chaos, adaptive systems, genetic algorithms—for analyzing complexity and complex systems. There are updates throughout the book as well. These take into account important advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. The chapter Economic Reality has also been revised to reflect a change in emphasis in Simon's thinking about the respective roles of organizations and markets in economic systems. |
| evolutionary optimization algorithms dan simon: Computational Complexity Sanjeev Arora, Boaz Barak, 2009-04-20 New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students. |
| evolutionary optimization algorithms dan simon: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. |
| evolutionary optimization algorithms dan simon: Artificial Intelligence in Asset Management Söhnke M. Bartram, Jürgen Branke, Mehrshad Motahari, 2020-08-28 Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity. |
| evolutionary optimization algorithms dan simon: Advances in Industrial and Production Engineering Kripa Shanker, Ravi Shankar, Rahul Sindhwani, 2019-04-23 This book comprises select proceedings of the International Conference on Future Learning Aspects of Mechanical Engineering (FLAME 2018). The book discusses different topics of industrial and production engineering such as sustainable manufacturing systems, computer-aided engineering, rapid prototyping, manufacturing management and automation, metrology, manufacturing process optimization, casting, welding, machining, and machine tools. The contents of this book will be useful for researchers as well as professionals. |
| evolutionary optimization algorithms dan simon: Nature-Inspired Computation in Engineering Xin-She Yang, 2016-03-19 This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining. |
| evolutionary optimization algorithms dan simon: Biogeography-Based Optimization: Algorithms and Applications Yujun Zheng, Xueqin Lu, Minxia Zhang, Shengyong Chen, 2018-09-14 This book introduces readers to the background, general framework, main operators, and other basic characteristics of biogeography-based optimization (BBO), which is an emerging branch of bio-inspired computation. In particular, the book presents the authors’ recent work on improved variants of BBO, hybridization of BBO with other algorithms, and the application of BBO to a variety of domains including transportation, image processing, and neural network learning. The content will help to advance research into and application of not only BBO but also the whole field of bio-inspired computation. The algorithms and applications are organized in a step-by-step manner and clearly described with the help of pseudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the algorithms to the engineering optimization problems they actually encounter. |
| evolutionary optimization algorithms dan simon: Applications of Metaheuristics in Process Engineering Jayaraman Valadi, Patrick Siarry, 2014-08-07 Metaheuristics exhibit desirable properties like simplicity, easy parallelizability and ready applicability to different types of optimization problems such as real parameter optimization, combinatorial optimization and mixed integer optimization. They are thus beginning to play a key role in different industrially important process engineering applications, among them the synthesis of heat and mass exchange equipment, synthesis of distillation columns and static and dynamic optimization of chemical and bioreactors. This book explains cutting-edge research techniques in related computational intelligence domains and their applications in real-world process engineering. It will be of interest to industrial practitioners and research academics. |
| evolutionary optimization algorithms dan simon: Metaheuristics Karl F. Doerner, Michel Gendreau, Peter Greistorfer, Walter Gutjahr, Richard F. Hartl, Marc Reimann, 2007-08-13 The aim of Metaheuristics: Progress in Complex Systems Optimization is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field. Highlighted are recent developments in the areas of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms. |
| evolutionary optimization algorithms dan simon: The Compatibility of Evolution and Design E. V. R. Kojonen, 2021-08-10 This book challenges the widespread assumption of the incompatibility of evolution and the biological design argument. Kojonen analyzes the traditional arguments for incompatibility, and argues for salvaging the idea of design in a way that is fully compatible with evolutionary biology. Relating current views to their intellectual history, Kojonen steers a course that avoids common pitfalls such as the problems of the God of the gaps, the problem of natural evil, and the traditional Humean and Darwinian critiques. The resulting deconstruction of the opposition between evolution and design has the potential to transform this important debate. |
| evolutionary optimization algorithms dan simon: How the Body Shapes the Way We Think Rolf Pfeifer, Josh Bongard, 2006-10-27 An exploration of embodied intelligence and its implications points toward a theory of intelligence in general; with case studies of intelligent systems in ubiquitous computing, business and management, human memory, and robotics. How could the body influence our thinking when it seems obvious that the brain controls the body? In How the Body Shapes the Way We Think, Rolf Pfeifer and Josh Bongard demonstrate that thought is not independent of the body but is tightly constrained, and at the same time enabled, by it. They argue that the kinds of thoughts we are capable of have their foundation in our embodiment—in our morphology and the material properties of our bodies. This crucial notion of embodiment underlies fundamental changes in the field of artificial intelligence over the past two decades, and Pfeifer and Bongard use the basic methodology of artificial intelligence—understanding by building—to describe their insights. If we understand how to design and build intelligent systems, they reason, we will better understand intelligence in general. In accessible, nontechnical language, and using many examples, they introduce the basic concepts by building on recent developments in robotics, biology, neuroscience, and psychology to outline a possible theory of intelligence. They illustrate applications of such a theory in ubiquitous computing, business and management, and the psychology of human memory. Embodied intelligence, as described by Pfeifer and Bongard, has important implications for our understanding of both natural and artificial intelligence. |
| evolutionary optimization algorithms dan simon: Molecular Evolution Roderick D.M. Page, Edward C. Holmes, 2009-07-14 The study of evolution at the molecular level has given the subject of evolutionary biology a new significance. Phylogenetic 'trees' of gene sequences are a powerful tool for recovering evolutionary relationships among species, and can be used to answer a broad range of evolutionary and ecological questions. They are also beginning to permeate the medical sciences. In this book, the authors approach the study of molecular evolution with the phylogenetic tree as a central metaphor. This will equip students and professionals with the ability to see both the evolutionary relevance of molecular data, and the significance evolutionary theory has for molecular studies. The book is accessible yet sufficiently detailed and explicit so that the student can learn the mechanics of the procedures discussed. The book is intended for senior undergraduate and graduate students taking courses in molecular evolution/phylogenetic reconstruction. It will also be a useful supplement for students taking wider courses in evolution, as well as a valuable resource for professionals. First student textbook of phylogenetic reconstruction which uses the tree as a central metaphor of evolution. Chapter summaries and annotated suggestions for further reading. Worked examples facilitate understanding of some of the more complex issues. Emphasis on clarity and accessibility. |
| evolutionary optimization algorithms dan simon: Interdisciplinary Advances in Information Technology Research Khosrow-Pour, D.B.A., Mehdi, 2013-03-31 Over the last few decades, the constant developments in the IT field have expanded into nearly every discipline and aspect of life. Interdisciplinary Advances in Information Technology Research explores multiple fields and the research done as well as how they differentiate and relate to one another. This collection provides focused discussions from unique perspectives on the latest information technology research. Researchers, practitioners, and professionals will benefit from this publication’s broad perspective. |
| evolutionary optimization algorithms dan simon: Modern Approaches in Applied Intelligence Kishan G. Mehrotra, Chilukuri Krishna Mohan, Jae C. Oh, Pramod K. Varshney, Moonis Ali, 2011-06-28 The two volume set LNAI 6703 and LNAI 6704 constitutes the thoroughly refereed conference proceedings of the 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, held in Syracuse, NY, USA, in June/July 2011. The total of 92 papers selected for the proceedings were carefully reviewed and selected from 206 submissions. The papers cover a wide number of topics including feature extraction, discretization, clustering, classification, diagnosis, data refinement, neural networks, genetic algorithms, learning classifier systems, Bayesian and probabilistic methods, image processing, robotics, navigation, optimization, scheduling, routing, game theory and agents, cognition, emotion, and beliefs. |
| evolutionary optimization algorithms dan simon: Nature-inspired Metaheuristic Algorithms Xin-She Yang, 2010 Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers. |
| evolutionary optimization algorithms dan simon: The Intelligent Design Debate and the Temptation of Scientism Erkki Vesa Rope Kojonen, 2016-04-28 The controversy over Intelligent Design (ID) has now continued for over two decades, with no signs of ending. For its defenders, ID is revolutionary new science, and its opposition is merely ideological. For its critics, ID is both bad science and bad theology. But the polemical nature of the debate makes it difficult to understand the nature of the arguments on all sides. A balanced and deep analysis of a controversial debate, this volume argues that beliefs about the purposiveness or non-purposiveness of nature should not be based merely on science. Rather, the philosophical and theological nature of such questions should be openly acknowledged. |
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Feb 20, 2014 · Introduction: I've been writing about the perfect PCT stack for a while, and it seems a lot of guys still have questions about the way to run the right kind of PCT. The perfect PCT is …
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