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Genetic Programming: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings (Lecture Notes in Computer Science #12101)
by Nuno Lourenço Ting Hu Eric Medvet Federico DivinaThis book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications.The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.
Genetic Programming
by James Mcdermott Mauro Castelli Lukas Sekanina Evert Haasdijk Pablo García-SánchezThis 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: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (Lecture Notes in Computer Science #13223)
by Eric Medvet Gisele Pappa Bing XueThis book constitutes the refereed proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022, held as part of Evo*2021, as Virtual Event, in April 2022, co-located with the Evo*2022 events, EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 35 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of complex combinational logic circuits.
Genetic Programming: 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (Lecture Notes in Computer Science #13986)
by Gisele Pappa Mario Giacobini Zdenek VasicekThis book constitutes the refereed proceedings of the 26th European Conference on Genetic Programming, EuroGP 2023, held as part of EvoStar 2023, in Brno, Czech Republic, during April 12–14, 2023, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications. The 14 revised full papers and 8 short papers presented in this book were carefully reviewed and selected from 38 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms for both optimization and machine learning problems as well as exploring GP to address complex real-world problems.
Genetic Programming: 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings (Lecture Notes in Computer Science #11451)
by Lukas Sekanina Ting Hu Nuno Lourenço Hendrik Richter Pablo García-SánchezThis book constitutes the refereed proceedings of the 22nd European Conference on Genetic Programming, EuroGP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and reflect the current state of research in the field. With a special focus on real-world applications in 2019, the papers are devoted to topics such as the test data design in software engineering, fault detection and classification of induction motors, digital circuit design, mosquito abundance prediction, machine learning and cryptographic function design.
Genetic Programming for Image Classification: An Automated Approach to Feature Learning (Adaptation, Learning, and Optimization #24)
by Mengjie Zhang Ying Bi Bing XueThis book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
Genetic Programming for Production Scheduling: An Evolutionary Learning Approach (Machine Learning: Foundations, Methodologies, and Applications)
by Fangfang Zhang Su Nguyen Yi Mei Mengjie ZhangThis book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP’s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
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.
Genetic Programming Theory and Practice XII
by Rick Riolo William P. Worzel Mark KotanchekThese 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: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer function, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. 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.
Genetic Programming Theory and Practice XIII
by Rick Riolo W. P. Worzel Mark Kotanchek Arthur KordonThese 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: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. 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.
Genetic Programming Theory and Practice XIV (Genetic and Evolutionary Computation)
by Rick Riolo Bill Worzel Brian Goldman Bill TozierThese 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. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic RegressionHybrid Structural and Behavioral Diversity Methods in GPMulti-Population Competitive Coevolution for Anticipation of Tax EvasionEvolving Artificial General Intelligence for Video Game ControllersA Detailed Analysis of a PushGP RunLinear Genomes for Structured ProgramsNeutrality, Robustness, and Evolvability in GPLocal Search in GPPRETSL: Distributed Probabilistic Rule Evolution for Time-Series ClassificationRelational Structure in Program Synthesis Problems with Analogical ReasoningAn Evolutionary Algorithm for Big Data Multi-Class Classification ProblemsA Generic Framework for Building Dispersion Operators in the Semantic SpaceAssisting Asset Model Development with Evolutionary AugmentationBuilding Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool 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.
Genetic Programming Theory and Practice XIX (Genetic and Evolutionary Computation)
by Leonardo Trujillo Stephan M. Winkler Sara Silva Wolfgang BanzhafThis book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year´s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the art in GP research.
Genetic Programming Theory and Practice XV (Genetic and Evolutionary Computation)
by Wolfgang Banzhaf Randal S. Olson William Tozier 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: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. 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.
Genetic Programming Theory and Practice XVII (Genetic and Evolutionary Computation)
by Wolfgang Banzhaf Erik Goodman Leigh Sheneman Leonardo Trujillo Bill WorzelThese 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. In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. 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.
Genetic Programming Theory and Practice XVIII (Genetic and Evolutionary Computation)
by Leonardo Trujillo Wolfgang Banzhaf Bill Worzel Stephan WinklerThis book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. 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.
Genetic Programming Theory and Practice XX (Genetic and Evolutionary Computation)
by Stephan Winkler Leonardo Trujillo Charles Ofria Ting HuGenetic Programming Theory and Practice brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year’s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine, and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the- art in GP research.
The GENI Book
by Rick Mcgeer Mark Berman Chip Elliott Robert RicciThis book, edited by four of the leaders of the National Science Foundation's Global Environment and Network Innovations (GENI) project, gives the reader a tour of the history, architecture, future, and applications of GENI. Built over the past decade by hundreds of leading computer scientists and engineers, GENI is a nationwide network used daily by thousands of computer scientists to explore the next Cloud and Internet and the applications and services they enable, which will transform our communities and our lives. Since by design it runs on existing computing and networking equipment and over the standard commodity Internet, it is poised for explosive growth and transformational impact over the next five years. Over 70 of the builders of GENI have contributed to present its development, architecture, and implementation, both as a standalone US project and as a federated peer with similar projects worldwide, forming the core of a worldwide network. Applications and services enabled by GENI, from smarter cities to intensive collaboration to immersive education, are discussed. The book also explores the concepts and technologies that transform the Internet from a shared transport network to a collection of "slices" -- private, on-the-fly application-specific nationwide networks with guarantees of privacy and responsiveness. The reader will learn the motivation for building GENI and the experience of its precursor infrastructures, the architecture and implementation of the GENI infrastructure, its deployment across the United States and worldwide, the new network applications and services enabled by and running on the GENI infrastructure, and its international collaborations and extensions. This book is useful for academics in the networking and distributed systems areas, Chief Information Officers in the academic, private, and government sectors, and network and information architects.
Genius: Theory, History and Technique (Springer Praxis Books)
by Roberto ManzoccoGenius is a fascinating topic. Everyone has an opinion on it, but not a lot of clarity. Much has been written on the subject - biographies, autobiographies, technical books, popular science books, and practical manuals - but genius in all of its dimensions has yet to be addressed. This book seeks to remedy that. What follows is a work of significant breadth that hopes to facilitate a nuanced popular understanding of the definition of genius, examining all of the main theories and approaches regarding the nature and origin of brilliance, the cognitive path that geniuses follow, and the difference that exists between “geniuses” on one side and “normal people” on the other. Pragmatic indications surrounding this issue are also examined, regarding such questions as: is it possible to become a genius or is genius innate? If it is possible, what is the path – no doubt long and difficult – that one must take? Is there a method for becoming a genius that can be taught and learned? This book will appeal to anyone who has ever contemplated great ideas and works and wondered how they came into being.
The Genius Hour Guidebook: Fostering Passion, Wonder, and Inquiry in the Classroom
by Denise Krebs Gallit ZviPromote your students&’ creativity and get them excited about learning! In the second edition of this popular, practical book, authors Denise Krebs and Gallit Zvi show you how to implement Genius Hour, a time when students can develop their own inquiry-based projects around their passions and take ownership of their work. Brought to you by MiddleWeb and Routledge Eye On Education, the book takes you step-by-step through planning and teaching Genius Hour. You&’ll learn how to guide your students as they: ● inspire learning and brainstorm wonders; ● develop inquiry questions based on their interests; ● conduct research and experiments about their topic of choice; ● create presentations to teach their fellow students in creative ways; and ● present their finished product for a final assessment. This edition includes new chapters on managing your classroom projects and recommended books. Throughout the book you will find voices from the Genius Hour community sharing real life stories and inspiration. Appendices contain handy FAQs and ready-made lessons and resources. In addition, a companion website, www.geniushourguide.org, offers bonus materials and regular updates to support you as you implement Genius Hour in your own classroom.
Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World
by Cade Metz<P><P>THE UNTOLD TECH STORY OF OUR TIME <P><P> What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing. Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. <P><P> Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn’t drive and didn’t fly because he could no longer sit down—but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do. They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line. <P><P> Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question: How far will we let it go?
Genius Squad
by Catherine JinksNow that the Axis Institute for World Domination has been blown up; the founder, Dr. Phineas Darkkon, has died; and Prosper English (who enrolled Cadel in the first place) is in jail for myriad offenses, Cadel Piggott has round-the-clock surveillance so he'll be safe until he testifies against Prosper English. But nobody seems to want Cadel. Not Fiona, his social worker; not Saul Greeniaus, the detective assigned to protect him. When he is approached by the head of Genius Squad--a group formed to investigate GenoME, one of Darkkon's pet projects--Cadel is dubious Genius Squad can offer him a real home and all the technology his heart desires. But why can't he bring himself to tell Saul what the group is really up to? And how can Genius Squad protect Cadel once Prosper English breaks out of jail?
Genome Annotation (Chapman & Hall/CRC Computational Biology Series #46)
by Jung Soh Paul M.K. Gordon Christoph W. SensenThe success of individualized medicine, advanced crops, and new and sustainable energy sources requires thoroughly annotated genomic information and the integration of this information into a coherent model. A thorough overview of this field, Genome Annotation explores automated genome analysis and annotation from its origins to the challenges of next-generation sequencing data analysis.The book initially takes you through the last 16 years since the sequencing of the first complete microbial genome. It explains how current analysis strategies were developed, including sequencing strategies, statistical models, and early annotation systems. The authors then present visualization techniques for displaying integrated results as well as state-of-the-art annotation tools, including MAGPIE, Ensembl, Bluejay, and Galaxy. They also discuss the pipelines for the analysis and annotation of complex, next-generation DNA sequencing data. Each chapter includes references and pointers to relevant tools. As very few existing genome annotation pipelines are capable of dealing with the staggering amount of DNA sequence information, new strategies must be developed to accommodate the needs of today’s genome researchers. Covering this topic in detail, Genome Annotation provides you with the foundation and tools to tackle this challenging and evolving area. Suitable for both students new to the field and professionals who deal with genomic information in their work, the book offers two genome annotation systems on an accompanying downloadable resources.
Genomics and Bioinformatics
by Tore SamuelssonWith the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.
Genomics-Enabled Learning Health Care Systems: Workshop Summary
by Sarah H. BeachyThe inclusion of genomic data in a knowledge-generating health care system infrastructure is one promising way to harness the full potential of that information to provide better patient care. In such a system, clinical practice and research influence each other with the goal of improving the efficiency and effectiveness of disease prevention, diagnosis, and treatment. To examine pragmatic approaches to incorporating genomics in learning health care systems, the Institute of Medicine Roundtable on Translating Genomic-Based Research for Health hosted a workshop which convened a variety of stakeholder groups, including commercial developers, health information technology professionals, clinical providers, academic researchers, patient groups, and government and health system representatives, to present their perspectives and participate in discussions on maximizing the value that can be obtained from genomic information. The workshop examined how a variety of systems are capturing and making use of genomic data to generate knowledge for advancing health care in the 21st century. It also sought to evaluate the challenges, opportunities, and best practices for capturing or using genomic information in knowledge-generating health care systems. "Genomics-Enabled Learning Health Care Systems" summarizes the presentations and discussion of the workshop.
Genomics-Enabled Learning Health Care Systems: Workshop Summary
by Sarah H. BeachyThe inclusion of genomic data in a knowledge-generating health care system infrastructure is one promising way to harness the full potential of that information to provide better patient care. In such a system, clinical practice and research influence each other with the goal of improving the efficiency and effectiveness of disease prevention, diagnosis, and treatment. To examine pragmatic approaches to incorporating genomics in learning health care systems, the Institute of Medicine Roundtable on Translating Genomic-Based Research for Health hosted a workshop which convened a variety of stakeholder groups, including commercial developers, health information technology professionals, clinical providers, academic researchers, patient groups, and government and health system representatives, to present their perspectives and participate in discussions on maximizing the value that can be obtained from genomic information. The workshop examined how a variety of systems are capturing and making use of genomic data to generate knowledge for advancing health care in the 21st century. It also sought to evaluate the challenges, opportunities, and best practices for capturing or using genomic information in knowledge-generating health care systems. Genomics-Enabled Learning Health Care Systems summarizes the presentations and discussion of the workshop.