Parallel Genetic Algorithms

This Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize po

This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approxi

Self-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,

Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of p

Parallel and Distributed Computing Applications examines various dimensions of parallel and distributed computing applications along with various computing algorithms required for programming designs.

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

This book describes the methods and algorithms for image pre-processing and recognition. These methods are based on a parallel shift technology of the imaging copy, as well as simple mathematical oper

The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many st

Recursion and self-embedding are at the heart of our ability to formulate our thoughts, articulate our imagination and share with other human beings. Nonetheless, controversy exists over the extent to which recursion is shared across all domains of syntax. A collection of 18 studies are presented here on the central linguistic property of recursion, examining a range of constructions in over a dozen languages representing great areal, typological and genetic diversity and spanning wide latitudes. The volume expands the topic to include prepositional phrases, possessives, adjectives, and relative clauses - our many vehicles to express creative thought - to provide a critical perspective on claims about how recursion connects to broader aspects of the mind. Parallel explorations across language families, literate and non-literate societies, children and adults are investigated and constitutes a new step in the generative tradition by simultaneously focusing on formal theory, acquisition

The bestselling author of The Accidental Billionaires and The 37th Parallel tells the fascinating Jurassic Park­-like story of the genetic restoration of an extinct species—the woolly mammo

Soft computing methods such as neural networks and genetic algorithms draw on the problem solving strategies of the natural world which differ fundamentally from the mathematically-based computing met

Written with the MRCOG examination requirements at its core, this book offers an extensive and up-to-date review of the full range of gynaecological cancers. The principles of epidemiology, imaging and treatment modalities are presented in a succinct and clinically focused manner. Each gynaecological cancer features in a stand-alone chapter, incorporating information on clinical issues, staging and principles of management. Genetic conditions and surgical principles are analysed, as well as topics such as palliative care, holistic approaches and communication. RCOG guidelines and Scientific Advisory Committee advice feature throughout the book. While authoritative and factual, the text is concise and clear. Flow-charts and diagrams support the assimilation of complex information, while practical tips, management algorithms, and clinical images enhance each chapter. Essential for those preparing for the MRCOG examination, this book is a comprehensive resource for health professionals wo

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo