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Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play
by David FosterGenerative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.Discover how VAEs can change facial expressions in photosTrain GANs to generate images based on your own datasetBuild diffusion models to produce new varieties of flowersTrain your own GPT for text generationLearn how large language models like ChatGPT are trainedExplore state-of-the-art architectures such as StyleGAN2 and ViT-VQGANCompose polyphonic music using Transformers and MuseGANUnderstand how generative world models can solve reinforcement learning tasksDive into multimodal models such as DALL.E 2, Imagen, and Stable DiffusionThis book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
by David FosterGenerative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos; Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation; Create recurrent generative models for text generation and learn how to improve the models using attention; Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting; Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN.
Generative Intelligence and Intelligent Tutoring Systems: 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10–13, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14798)
by Angelo Sifaleras Fuhua LinThis book constitutes the refereed proceedings of the 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024, held in Thessaloniki, Greece, during June 10–13, 2024. The 35 full papers and 28 short papers included in this book were carefully reviewed and selected from 88 submissions. This book also contains 2 invited talks. They were organized in topical sections as follows: Generative Intelligence and Tutoring Systems; Generative Intelligence and Healthcare Informatics; Human Interaction, Games and Virtual Reality; Neural Networks and Data Mining; Generative Intelligence and Metaverse; Security, Privacy and Ethics in Generative Intelligence; and Generative Intelligence for Applied Natural Language Processing.
Generative Intelligence and Intelligent Tutoring Systems: 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10–13, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14799)
by Angelo Sifaleras Fuhua LinThis book constitutes the refereed proceedings of the 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024, held in Thessaloniki, Greece, during June 10–13, 2024. The 35 full papers and 28 short papers included in this book were carefully reviewed and selected from 88 submissions. This book also contains 2 invited talks. They were organized in topical sections as follows: Generative Intelligence and Tutoring Systems; Generative Intelligence and Healthcare Informatics; Human Interaction, Games and Virtual Reality; Neural Networks and Data Mining; Generative Intelligence and Metaverse; Security, Privacy and Ethics in Generative Intelligence; and Generative Intelligence for Applied Natural Language Processing.
Generative KI als neues Teammitglied im Marketing: Ein Leitfaden für Marketingmanger:innen (essentials)
by Bernhard WeckeGenerative Künstliche Intelligenz bringt die digitale Transformation im Marketing auf eine neue Ebene und stellt Marketingverantwortliche vor unbekannte Herausforderungen. Erfahren Sie, wie sich die Rolle von Marketingmanager:innen in Zeiten von Generativer KI verändert und welche neuen Kompetenzen gefragt sind. Darüber hinaus gibt das Buch einen Überblick über die Auswirkungen von Generativer KI auf Marketingorganisationen und einen konkreten Leitfaden zu den Handlungsfeldern. .
Generative KI für Dummies (Für Dummies)
by Pam BakerDie Möglichkeiten generativer KI optimal nutzen Mithilfe von generativer Künstlicher Intelligenz lassen sich Texte, Bilder, Videos, Sprache, Musik, Softwarecodes und auch Kunst und Design erschaffen. Doch wie können Sie die Möglichkeiten generativer KI individuell am besten nutzen? KI-Expertin Pam Baker gibt Ihnen einen guten Überblick und führt Sie in die bekanntesten Tools ein. Das Buch erklärt Ihnen, wie Sie geeignete Plattformen finden, gute Prompts schreiben, KI in Ihren Workflow einbinden und gezielt einsetzen – und entscheiden, wann doch ein wenig Human Touch gebraucht wird. Gut verständlich und praxisnah: der ideale Einstieg in ein Thema, das unsere Arbeitswelt rasant verändert. Sie erfahren Welche Plattformen sich für welche Zwecke eignen Wie Sie KI-generierte Texte, Bilder, Videos und Audios erstellen Wie Sie KI in Ihren Workflow integrieren Wie es Ihnen gelingt, KI-Inhalte »menschlicher« zu machen
Generative Künstliche Intelligenz in Marketing und Sales: Innovative Unternehmenspraxis: Insights, Strategien und Impulse
by Gabriele Schuster Thomas BolzDieses Buch beleuchtet die neuesten Entwicklungen in der Generativen KI und zeigt auf, wie Marketing und Vertrieb sie für sich nutzen können. KI verändert nicht nur einzelne Werkzeuge und Arbeitsweisen, sondern leitet einen grundlegenden Paradigmenwechsel in beiden Bereichen ein. Die Autor:innen bieten in ihren Beiträgen einen fundierten und umfassenden Einblick in aktuelle Anwendungsbereiche und Forschungsansätze zu Generativer KI im Marketing und Vertrieb. Thematisiert werden zahlreiche erfolgskritische Handlungsfelder, u. a. datenschutzrechtliche Herausforderungen, Social-Media- und Content-Marketing mit KI, Chatbots in der Employee-Journey, hyperpersonalisierte B2C-Kommunikation, KI-generierte Produkt- und SEO-Texte, bis hin zu Generativer KI in der Marketingforschung und die Rolle von KI in Start-ups. Das Werk richtet sich sowohl an Praktiker:innen und Entscheidungsträger:innen als auch an Forschende sowie Studierende und trägt dazu bei, das Verständnis für dieses dynamische und vielversprechende Feld zu vertiefen. Mit Beiträgen von: Prof. Dr. Petra Beenken Luise-Sophie Bleckmann Prof. Dr. Benny Björn Briesemeister Prof. Dr. Marc Ebel Susanne Fittkau Caroline Grauel Prof. Dr. Maik Günther Doris Hanway Prof. Dr. Claudia Heß Heiko Holz Isabel Hörmann Prof Dr. rer. pol. Kurt Jeschke Prof. Dr. Tobias Kesting Prof. Dr. Sonja Klose Prof. Dr. Ralf Kneuper Prof. Dr. Ralf T. Kreutzer Prof. Dr. Nicolai Krüger Prof. Dr. Sibylle Kunz Prof. Dr. Visieu Lac Christina Lagodka Prof. Dr. Alexander Lawall Prof. Dr. Damian Leschik Prof. Dr. Vera Lenz-Kesekamp Prof. Dr. Nikolaj Lunze Prof. Dr. Jan-Paul Lüdtke Elisabeth L’ Orange Sebastian Mertens Manuel Muth Prof. Dr. Gerd Nufer Prof. Dr. Miriam O’Shea Ban Pahlawan Florian Perst Marc Peter Prof. Dr. Jan Pieper Lavinia Celina Rahmawati Prof. Dr. Katharina-Maria Rehfeld Marie Salkowski Prof. Dr. Philipp Schmid Annkristin Schürhaus Freddy Sikouonmeu Neal Wessling Prof. Dr. Klaus Wildhirt Prof. Dr. Atilla Wohllebe Cansu Rosa Yildirim Prof. Dr. Tanja Marlen Zweigle
Generative Machine Learning Models in Medical Image Computing
by Chen Chen Le Zhang Zeju Li Greg SlabaughGenerative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics. Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing.
Genere ingresos pasivos con quora: Y olvidese de su trabajo
by Marcus PfeifferEste libro trata sobre cómo ganar dinero bajo un sistema de recomendaciones de productos y servicios a través de la plataforma de quora, haciendo ingresos pasivos en línea.
Generic Data Structures and Algorithms in Go: An Applied Approach Using Concurrency, Genericity and Heuristics
by Richard WienerAdvance your understanding of generic data structures and algorithms and their applications using Go and the effective use of concurrency. You are invited on a journey that aims to improve your programming and problem-solving skills. This book takes you to the next step by showing how to get your programs to work efficiently as well as correctly. As you explore many data structures and the algorithms and applications associated with them, you'll focus on the trade-offs between speed and storage and the benefits of deploying concurrency when appropriate. This book will demonstrate the huge increases in application performance that are possible. The presentation of classic data structures and techniques of algorithm design (greedy, divide and conquer, branch-and-bound to name a few) provides an essential foundation and toolkit for problem solving. But this book goes further by presenting heuristic algorithms and their implementations for solving computationally intractable combinatoric optimization problems such as the travelling salesperson problem. Simulated annealing and genetic algorithms are among the techniques used.The consistent style of coding used throughout this book exploits Go’s ability to implement abstract, generic and constrained generic data types without the use of classes. Although some familiarity with Go is assumed, this book should advance your ability to use Go to tackle server-side applications, games, machine learning, information retrieval and other application domains where speed and storage efficiency is essential.What You'll LearnExplore classical data structures and algorithms aimed at making your applications run faster or require less storageUse the new generic features of Go to build reusable data structuresUtilize concurrency for maximizing application performanceSee the power of heuristic algorithms for computationally intractable problemsEnhance and improve your Go programming skillsWho This Book Is ForPracticing Go software developers and students who wish to advance their programming and problem-solving skills and experience the excitement and see the benefits of using generic data structures and algorithms that utilize concurrency whenever possible.
Generic Pipelines Using Docker: The DevOps Guide to Building Reusable, Platform Agnostic CI/CD Frameworks
by Brandon Atkinson Dallas EdwardsCreate generic pipelines to reduce your overall DevOps workload and allow your team to deliver faster. This book helps you get up to speed on the pros and cons of generic pipeline methodology, and learn to combine shell scripts and Docker to build generic pipelines.In today’s world of micro-services and agile practices, DevOps teams need to move as fast as feature teams. This can be extremely challenging if you’re creating multiple pipelines per application or tech stack. What if your feature teams could utilize a generic pipeline that could build, test, and deploy any application, regardless of tech stack? What if that pipeline was also cloud and platform agnostic? Too good to be true? Well think again! Generic Pipelines Using Docker explores the principles and implementations that allow you to do just that. You will learn from real-world examples and reusable code. After reading this book you will have the knowledge to build generic pipelines that any team can use. What You'll Learn Explore the pros and cons of generic pipeline methodologyCombine shell scripts and Docker to build a generic pipelineImplement a pipeline across CI/CD platformsBuild a pipeline that lends itself well to both centralized and federated DevOps teamsConstruct a modular pipeline with components that can be added, removed, or replaced as needed Who This Book Is ForProfessionals who use DevOps or are part of a DevOps team, and are seeking ways to streamline their pipelines and drive more deployments while using less code
Generic and Energy-Efficient Context-Aware Mobile Sensing
by Chi Harold Liu Ozgur YururElaborating on the concept of context awareness, this book presents up-to-date research and novel framework designs for context-aware mobile sensing. Generic and Energy-Efficient Context-Aware Mobile Sensing proposes novel context-inferring algorithms and generic framework designs that can help readers enhance existing tradeoffs in mobile sensing,
Geneses of Postmodern Art: Technology As Iconology (Routledge Advances in Art and Visual Studies)
by Paul CrowtherPostmodernism in the visual arts is not just another 'ism.' It emerged in the 1960s as a transformation of artistic creativity inspired by Duchamp's idea that the artwork does not have to be physically made by its creator. Products of mass culture and technology can be used just as well as traditional media. This idea became influential because of a widespread naturalization of technology - where technology becomes something lived in as well as used. Postmodern art embodies this attitude. To explain why, Paul Crowther investigates topics such as eclecticism, the sublime, deconstruction in art and philosophy, and Paolozzi's Wittgenstein-inspired works.
Genesis: Artificial Intelligence, Hope, and the Human Spirit
by Henry A. Kissinger Eric Schmidt Craig MundieIn his final book, the late Henry Kissinger joins forces with two leading technologists to mount &“a profound exploration&” (Walter Isaacson) of the epochal challenges and opportunities presented by the revolution in Artificial Intelligence: a breakthrough that dramatically empowers people in all walks of life while also raising urgent questions about the future of humanity. As it absorbs data, gains agency, and intermediates between humans and reality, AI (Artificial Intelligence) will help us to address enormous crises, from climate change to geopolitical conflicts to income inequality. It might well solve some of the greatest mysteries of our universe and elevate the human spirit to unimaginable heights. But it will also pose challenges on a scale and of an intensity that we have never seen—usurping our power of independent judgment and action, testing our relationship with the divine, and perhaps even spurring a new phase in human evolution. The last book of elder statesman Henry Kissinger, written with technologists Craig Mundie and Eric Schmidt, Genesis charts a course between blind faith and unjustified fear as it outlines an effective strategy for navigating the age of AI.
Genesis: Artificial Intelligence, Hope, and the Human Spirit
by Henry A. Kissinger Eric Schmidt Craig MundieTHE FOLLOW UP TO THE INTERNATIONAL BESTSELLER THE AGE OF AI In his final book, the late Henry Kissinger joins forces with two leading technologists to mount a profound exploration of the epochal challenges and opportunities presented by the revolution in Artificial Intelligence.As it absorbs data, gains agency, and intermediates between humans and reality, AI (Artificial Intelligence) will help us to address enormous crises, from climate change to geopolitical conflicts to income inequality. It might well solve some of the greatest mysteries of our universe and elevate the human spirit to unimaginable heights. But it will also pose challenges on a scale and of an intensity that we have never seen - usurping our power of independent judgment and action, testing our relationship with the divine, and perhaps even spurring a new phase in human evolution.The last book of elder statesman Henry Kissinger, written with technologists Craig Mundie and Eric Schmidt, Genesis charts a course between blind faith and unjustified fear as it outlines an effective strategy for navigating the age of AI.
Genetic Algorithm Essentials
by Oliver KramerThis book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights)
by Stefan Wagner Stephan Winkler Michael Affenzeller Andreas BehamGenetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
Genetic Algorithms and Machine Learning for Programmers
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods, including tournament and roulette wheels.Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. <P><P> Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
Genetic Algorithms for Pattern Recognition (CRC Press Revivals)
by Sankar K. Pal Paul P. WangSolving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
Genetic Algorithms in Elixir: Solve Problems Using Evolution
by Sean MoriarityFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.
Genetic Algorithms in Java Basics
by Lee Jacobson Burak KanberGenetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own. Genetic algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research. Guides you through the theory behind genetic algorithms Explains how genetic algorithms can be used for software developers trying to solve a range of problems Provides a step-by-step guide to implementing genetic algorithms in Java What you'll learn How to construct genetic algorithms in Java which you can extend for your own projects and research What genetic algorithms are and the biological inspiration behind them How genetic algorithms can be implemented to solve problems Solving a traveling salesman problem and how to apply a genetic algorithm to it How to use a genetic algorithm to solve timetabling problems How a genetic algorithm can be used to build a robotic controller Applying optimization techniques to genetic algorithms Who this book is for Genetic Algorithms in Java Basics is perfect for developers, researchers and students who are working on problems where genetic algorithms may be a solution and need to program real, working code in Java. It's also suitable for all Java developers who are curious about genetic algorithms who would like a practical, hands-on introduction to genetic algorithms using Java. Table of Contents 1. Introduction 2. Implementation of a Basic Algorithm 3. Robot Controllers 4. Traveling 5. Class Scheduling 6. Optimization
Genetic Programming
by Malcolm I. Heywood James Mcdermott Mauro Castelli Kevin Sim Ernesto CostaThis book constitutes the refereed proceedings of the19th European Conference on Genetic Programming, EuroGP 2016, held in Porto,Portugal, in March/April 2016 co-located with the Evo*2016 events: EvoCOP,EvoMUSART, and EvoApplications. The 11 revised full papers presented togetherwith 8 poster papers were carefully reviewed and selected from 36 submissions. The wide range of topics in this volume reflects the current state of researchin the field. Thus, we see topics as diverse as semantic methods, recursiveprograms, grammatical methods, coevolution, Cartesian GP, feature selection,metaheuristics, evolvability, and fitness predictors; and applicationsincluding image processing, one-class classification, SQL injection attacks,numerical modelling, streaming data classification, creation and optimisationof circuits, multi-class classification, scheduling in manufacturing andwireless networks.
Genetic Programming
by James Mcdermott Mauro Castelli Pablo García-Sánchez Lukas Sekanina Evert HaasdijkThis book constitutes the refereed proceedings of the 18th European Conference on Genetic Programming, EuroGP 2015, held in Copenhagen, Spain, in April 2015 co-located with the Evo 2015 events, EvoCOP, Evo MUSART and Evo Applications. The 12 revised full papers presented together with 6 poster papers were carefully reviewed and selected form 36 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics as diverse as semantic methods, recursive programs, grammatical methods, coevolution, Cartesian GP, feature selection, initialisation procedures, ensemble methods and search objectives; and applications including text processing, cryptography, numerical modelling, software parallelisation, creation and optimisation of circuits, multi-class classification, scheduling and artificial intelligence.
Genetic Programming Theory and Practice X
by Ekaterina Vladislavleva Marylyn D Ritchie Jason H. Moore Rick RioloThese contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud - communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions - model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining different types of data. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.