Search results for "Data Structures and Algorithms in Java"
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New Frontier In Evolutionary Algorithms: Theory And Applications
This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic concepts, swarm intelligence, and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting, and data mining in bioinformatics.The emphasis of this book is on applicability to the real world. Tasks from application areas - optimization of the trading rule in foreign exchange (FX) and stock prices, economic load dispatch in power system, exit/door placement for evacuation planning, and gene regulatory network inference in bioinformatics - are studied, and the resultant empirical investigations demonstrate how successful the proposed approaches are when solving real-world tasks of great importance.
R 2,367.05
Introduction to the Design and Analysis of Algorithms
Based on a new classification of algorithm design techniques and a clear delineation of analysis methods, Introduction to the Design and Analysis of Algorithms presents the subject in a coherent and innovative manner. Written in a student-friendly style, the book emphasizes the understanding of ideas over excessively formal treatment while thoroughly covering the material required in an introductory algorithms course. Popular puzzles are used to motivate students' interest and strengthen their skills in algorithmic problem solving. Other learning-enhancement features include chapter summaries, hints to the exercises, and a detailed solution manual.
R 6,473.00
Evolutionary Algorithms
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.
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Parameterized Algorithms
This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way.The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds.All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.
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Analysis of Algorithms
This text teaches the techniques needed to analyze algorithms. Organized by analysis techniques, The Analysis of Algorithms includes a systematic and largely self-contained treatment of the mathematics needed for elementary and intermediate analyses, as well as brief guides to the sources for more advanced techniques. Each technique is illustrated by being applied to the analysis of a realistic algorith. The authors provide explicit guidance on the use of various methods-for example, the discussion of mathematical induction emphasizes the process of finding the induction hypothesis, and the chapter on formulas for simplifying summations includes a section on deciding which formulas to apply. Many of the exercises give the student an opportunity to apply the techniques in developing original algorithm analyses.
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Parameterized Algorithms
This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way.The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds.All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.
R 2,198.00
Algorithmen
Zu den wichtigen und fundamentalen Konzepten der Informatik gehört sicher das des Algorithmus. Seit man sich mit Maschinen beschäftigt, die einfache mathematische Operationen ausführen können, befaßt man sich mit den Problemen, was überhaupt berechnet werden kann und wie diese Berechnungen effizient durchgeführt werden können. Durch die Erfindung des Computers wurde die Behandlung dieser Fragen stark ge fördert; dies hat zur Entdeckung vieler wichtiger und ideenreicher Algorithmen geführt. Das Studium von Algorithmen ist ein besonderes Anliegen der Informatik. In diesem Buch wollen wir die Kenntnisse über Algorithmen in zusammenhängender Weise so darstellen, daß sowohl Stu denten als auch in der Praxis Tätige den Entwurf und die Analyse neuer Algorithmen erlernen können. Ein Buch, das jeden Algorithmus enthält, der jemals erfunden wur de, müßte einen enormen Umfang haben. Daher beschränkt man sich übli cherweise bei Büchern über Algorithmen auf wenige Problembereiche, die dann im Detail behandelt werden. Zu jedem speziellen Problem wird der effizienteste Lösungsalgorithmus vorgestellt und analysiert. Da wir mehrere Jahre lang Kurse nach dieser Methode abgehalten haben, kennen wir den großen Nachteil dieses Verfahrens. Der Student lernt zwar viele schnelle Algorithmen kennen und kann diese auch analysie ren, im Entwurf guter Algorithmen bleibt er aber unsicher.
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