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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 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 and Evolutionary Computing: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computing (Volume I), October 6–8, 2023, Kaohsiung, Taiwan (Lecture Notes in Electrical Engineering #1145)
by Jerry Chun-Wei Lin Chin-Shiuh Shieh Mong-Fong Horng Shu-Chuan ChuThis first book of conference proceedings contains selected papers presented at ICGEC 2023, the 15th International Conference on Genetic and Evolutionary Computing, held on October 6–8, 2023, in Kaohsiung, Taiwan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers know the up-to-date techniques of the mentioned topics, including swarm intelligence and its applications, operational technologies and networked multimedia applications, wearable computing and intelligent data hiding, image processing and intelligent applications, and intelligent multimedia tools and applications. It helps readers bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic and Evolutionary Computing: Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computing (Volume II), October 6-8, 2023, Kaohsiung, Taiwan (Lecture Notes in Electrical Engineering #1114)
by Jeng-Shyang Pan Zhigeng Pan Pei Hu Jerry Chun-Wei LinThis second volume of conference proceedings contains selected papers presented at ICGEC 2023, the 15th International Conference on Genetic and Evolutionary Computing, held on October 6-8, 2023 in Kaohsiung, Taiwan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may know the up-to-date techniques of the mentioned topics, including technologies for next-generation network environments, recent progress in computational electromagnetic dynamics, future cyber security, privacy and forensics for advanced systems, data mining techniques and its applications, optimization models in deep learning and machine learning. It will help readers bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic and Evolutionary Computing: Proceedings of the Sixteenth International Conference on Genetic and Evolutionary Computing, August 28-30, 2024, Miyazaki, Japan (Volume 1) (Lecture Notes in Electrical Engineering #1321)
by Jeng-Shyang Pan Thi Thi Zin Tien-Wen Sung Jerry Chun-Wei LinThis book contains accepted papers presented at ICGEC 2024, the 16th International Conference on Genetic and Evolutionary Computing, held from August 28-29, 2024 in Miyazaki, Japan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may know the up-to-date techniques of the mentioned topics, including digital transformation, machine learning and data analysis, meta-heuristic optimization algorithms, computer vision, and artificial intelligence of things (AIoT), which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic and Evolutionary Computing: Proceedings of the Sixteenth International Conference on Genetic and Evolutionary Computing, August 28-30, 2024, Miyazaki, Japan (Volume 2) (Lecture Notes in Electrical Engineering #1322)
by Jeng-Shyang Pan Thi Thi Zin Tien-Wen Sung Jerry Chun-Wei LinThis book contains accepted papers presented at ICGEC 2024, the 16th International Conference on Genetic and Evolutionary Computing, held from August 28-29, 2024 in Miyazaki, Japan. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may know the up-to-date techniques of the mentioned topics, including digital transformation, machine learning and data analysis, meta-heuristic optimization algorithms, computer vision, and artificial intelligence of things (AIoT), which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
Genetic Programming: 27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings (Lecture Notes in Computer Science #14631)
by Mario Giacobini Bing Xue Luca ManzoniThis book constitutes the refereed proceedings of the 27th European Conference on Genetic Programming, EuroGP 2024, held in Aberystwyth, UK, April 3–5, 2024 and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications.The 13 papers (9 selected for long presentation and 4 for short presentation) collected in this book were carefully reviewed and selected from 24 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, as well as exploring GP applications to the optimization of machine learning methods and the evolution of control policies.
Genetic Theory for Cubic Graphs
by Pouya Baniasadi Vladimir Ejov Jerzy A. Filar Michael HaythorpeThis book was motivated by the notion that some of the underlying difficulty in challenging instances of graph-based problems (e. g. , the Traveling Salesman Problem) may be "inherited" from simpler graphs which - in an appropriate sense - could be seen as "ancestors" of the given graph instance. The authors propose a partitioning of the set of unlabeled, connected cubic graphs into two disjoint subsets named genes and descendants, where the cardinality of the descendants dominates that of the genes. The key distinction between the two subsets is the presence of special edge cut sets, called cubic crackers, in the descendants. The book begins by proving that any given descendant may be constructed by starting from a finite set of genes and introducing the required cubic crackers through the use of six special operations, called breeding operations. It shows that each breeding operation is invertible, and these inverse operations are examined. It is therefore possible, for any given descendant, to identify a family of genes that could be used to generate the descendant. The authors refer to such a family of genes as a "complete family of ancestor genes" for that particular descendant. The book proves the fundamental, although quite unexpected, result that any given descendant has exactly one complete family of ancestor genes. This result indicates that the particular combination of breeding operations used strikes the right balance between ensuring that every descendant may be constructed while permitting only one generating set. The result that any descendant can be constructed from a unique set of ancestor genes indicates that most of the structure in the descendant has been, in some way, inherited from that, very special, complete family of ancestor genes, with the remaining structure induced by the breeding operations. After establishing this, the authors proceed to investigate a number of graph theoretic properties: Hamiltonicity, bipartiteness, and planarity, and prove results linking properties of the descendant to those of the ancestor genes. They develop necessary (and in some cases, sufficient) conditions for a descendant to contain a property in terms of the properties of its ancestor genes. These results motivate the development of parallelizable heuristics that first decompose a graph into ancestor genes, and then consider the genes individually. In particular, they provide such a heuristic for the Hamiltonian cycle problem. Additionally, a framework for constructing graphs with desired properties is developed, which shows how many (known) graphs that constitute counterexamples of conjectures could be easily found.
Genetically Modified Organisms, Grade 7: STEM Road Map for Middle School (STEM Road Map Curriculum Series)
by Carla C. Johnson Janet B. Walton Erin E. Peters-BurtonWhat if you could challenge your seventh graders to become informed citizens by analyzing real-world implications of GMOs? With this volume in the STEM Road Map Curriculum Series, you can! Genetically Modified Organisms outlines a journey that will steer your students toward authentic problem solving while grounding them in integrated STEM disciplines. Like the other volumes in the series, this book is designed to meet the growing need to infuse real-world learning into K–12 classrooms. This interdisciplinary, five-lesson module uses project- and problem-based learning to help students investigate the opportunities and challenges of GMO production and consumption. Working in teams, students will create a documentary communicating the health, social, and economic aspects of GMO production and consumption. To support this goal, students will do the following: • Use the Internet and other sources to build knowledge of an issue, and recognize and value stakeholders and their viewpoints in an issue. • Explore the relationship among local, state, and federal legislation related to GMOs. • Understand the role of cost-benefit analysis in making informed economic decisions. • Develop skills to evaluate arguments, create and communicate individual understanding and perspectives. • Gain a deeper understanding that structure and function are related by examining plants and how the environment and genetics influences structure. • Gain a better understanding of what tools humans have developed to genetically alter organisms for human benefit. The STEM Road Map Curriculum Series is anchored in the Next Generation Science Standards, the Common Core State Standards, and the Framework for 21st Century Learning. In-depth and flexible, Genetically Modified Organisms can be used as a whole unit or in part to meet the needs of districts, schools, and teachers who are charting a course toward an integrated STEM approach.
Genetics and Randomness
by null Anatoly RuvinskyAnalyzes Randomness in Major Genetic Processes and EventsNo matter how far science advances, the proportion of what is knowable to what is random will remain unchanged, and attempts to ignore this critical threshold are futile at best. With the revolutionary explosion in genetic information discovery, it is crucially important to recognize the unde
Geniale Frauen in der Wissenschaft: Versteckte Beiträge, die die Welt verändert haben
by Lars JaegerObwohl Frauen schon früh das wissenschaftliche Denken mitgeprägt haben, sichtbar geworden sind sie fast nie. Dieses Ungleichgewicht setzt sich bis heute fort, auch wenn es aktuell weit mehr Wissenschaftlerinnen gibt als jemals zuvor. Lars Jaeger spannt einen Bogen von der Antike bis heute und porträtiert in essayartigen Einführungen das Leben und Wirken der wohl bedeutendsten weiblichen Naturwissenschaftlerinnen und Mathematikerinnen. Von Hypatia von Alexandria über Émilie du Châtelet und Emmy Noether bis hin zu Lisa Randall, sie alle haben Großes geleistet, die Wissenschaft entscheidend vorangebracht und konnten dennoch oft nicht aus dem Schatten ihrer männlichen Kollegen treten.Neben den spannenden Porträts der einzelnen Wissenschaftlerinnen sowie einer detaillierten und anschaulichen Darstellung ihrer wissenschaftlichen Leistungen beleuchtet dieses Sachbuch auch das Geschlechterverhältnis in der Wissenschaft, das sich nur quälend langsam zugunsten eines fairen Verhältnisses für die Frauen entwickelt.
Genius at Play: The Curious Mind of John Horton Conway
by Siobhan RobertsA multifaceted biography of a brilliant mathematician and iconoclastA mathematician unlike any other, John Horton Conway (1937–2020) possessed a rock star&’s charisma, a polymath&’s promiscuous curiosity, and a sly sense of humor. Conway found fame as a barefoot professor at Cambridge, where he discovered the Conway groups in mathematical symmetry and the aptly named surreal numbers. He also invented the cult classic Game of Life, a cellular automaton that demonstrates how simplicity generates complexity—and provides an analogy for mathematics and the entire universe. Moving to Princeton in 1987, Conway used ropes, dice, pennies, coat hangers, and the occasional Slinky to illustrate his winning imagination and share his nerdish delights. Genius at Play tells the story of this ambassador-at-large for the beauties and joys of mathematics, lays bare Conway&’s personal and professional idiosyncrasies, and offers an intimate look into the mind of one of the twentieth century&’s most endearing and original intellectuals.
Genocide: State Power and Mass Murder (Issues In Contemporary Civilization Ser.)
by James BaldwinThis book is dedicated to a consideration of genocide in the context of political sociology. It demonstrates that the underlining predicates of sociology give scant consideration to basic issues of life and death in favor of distinctly derivative issues of social structure and social function.
Genome Data Analysis (Learning Materials in Biosciences)
by Ju Han KimThis textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing
by Veli Mäkinen Djamal Belazzougui Fabio Cunial Alexandru I. TomescuHigh-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows-Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Genomic Approaches for Cross-Species Extrapolation in Toxicology
by William H. Benson Richard T. Di GiulioThe latest tools for investigating stress response in organisms, genomic technologies provide great insight into how different organisms respond to environmental conditions. However, their usefulness needs to be tested, verified, and codified. Genomic Approaches for Cross-Species Extrapolation in Toxicology provides a balanced discussion drawn from
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 Data Analysis: False Discovery Rates and Empirical Bayes Methods
by David R. BickelStatisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
Genomics Data Analysis for Crop Improvement (Springer Protocols Handbooks)
by Priyanka Anjoy Kuldeep Kumar Girish Chandra Kishor GaikwadThis book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
Genomics in the Cloud: Using Docker, GATK, and WDL in Terra
by Geraldine A. Van der Auwera Brian D. O'ConnorData in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud?With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field.This book covers:Essential genomics and computing technology backgroundBasic cloud computing operationsGetting started with GATK, plus three major GATK Best Practices pipelinesAutomating analysis with scripted workflows using WDL and CromwellScaling up workflow execution in the cloud, including parallelization and cost optimizationInteractive analysis in the cloud using Jupyter notebooksSecure collaboration and computational reproducibility using Terra
The Gentle Art of Mathematics
by Dan PedoeMathematical games, probability, the question of infinity, topology, how the laws of algebra work, problems of irrational numbers, and more. 42 figures.
A Gentle Course in Local Class Field Theory: Local Number Fields, Brauer Groups, Galois Cohomology
by Pierre GuillotThis book offers a self-contained exposition of local class field theory, serving as a second course on Galois theory. It opens with a discussion of several fundamental topics in algebra, such as profinite groups, p-adic fields, semisimple algebras and their modules, and homological algebra with the example of group cohomology. The book culminates with the description of the abelian extensions of local number fields, as well as the celebrated Kronecker–Weber theory, in both the local and global cases. The material will find use across disciplines, including number theory, representation theory, algebraic geometry, and algebraic topology. Written for beginning graduate students and advanced undergraduates, this book can be used in the classroom or for independent study.
A Gentle Introduction to Optimization
by B. Guenin J. Könemann L. TunçelOptimization is an essential technique for solving problems in areas as diverse as accounting, computer science and engineering. Assuming only basic linear algebra and with a clear focus on the fundamental concepts, this textbook is the perfect starting point for first- and second-year undergraduate students from a wide range of backgrounds and with varying levels of ability. Modern, real-world examples motivate the theory throughout. The authors keep the text as concise and focused as possible, with more advanced material treated separately or in starred exercises. Chapters are self-contained so that instructors and students can adapt the material to suit their own needs and a wide selection of over 140 exercises gives readers the opportunity to try out the skills they gain in each section. Solutions are available for instructors. The book also provides suggestions for further reading to help students take the next step to more advanced material.
A Gentle Introduction to Scientific Computing (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)
by Dan Stanescu Long LeeScientific Computation has established itself as a stand-alone area of knowledge in the border area between computer science and applied mathematics. Nonetheless, its interdisciplinary character cannot be denied: its methodologies are increasingly used in a wide variety of branches of science and engineering. A Gentle Introduction to Scientific Computing intends to serve a very broad audience of college students across a variety of disciplines. It aims to expose its readers to some of the basic tools and techniques used in computational science, with a view to helping them understand what happens ‘behind the scenes’ when simple tools such as solving equations, plotting and interpolation are used. To make the book as practical as possible, the authors explore their subject both from a theoretical, mathematical perspective and from an implementation-driven, programming perspective. Features Takes a middle ground approach between theoretical book and implementation Suitable reading for a broad range of students in STEM disciplines, and could be the primary text for a first course in scientific computing Introduces mathematics majors, without any prior computer science exposure, to numerical methods All mathematical knowledge needed beyond Calculus (and the more useful Calculus notation and concepts) is introduced in the text to make it self-contained.
A Gentle Introduction To Stata
by Alan C. AcockAcock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book.