Michael N. Vrahatis's Books

Swarm Intelligence and Bio-Inspired Computation

10. Intelligent Music Composition

By: Maximos A. Kaliakatsos-Papakostas,Andreas Floros,Michael N. Vrahatis

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Swarm Intelligence and Bio-Inspired Computation

10. Intelligent Music Composition

By: Maximos A. Kaliakatsos-Papakostas,Andreas Floros,Michael N. Vrahatis

Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.

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Elsevier Inc. Chapters

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ISBN 10

0128068965

ISBN 13

9780128068960

Pyomo — Optimization Modeling in Python

By: Michael L. Bynum,Gabriel A. Hackebeil,William E. Hart,Carl D. Laird,Bethany L. Nicholson,John D. Siirola,Jean-Paul Watson,David L. Woodruff

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Pyomo — Optimization Modeling in Python

By: Michael L. Bynum,Gabriel A. Hackebeil,William E. Hart,Carl D. Laird,Bethany L. Nicholson,John D. Siirola,Jean-Paul Watson,David L. Woodruff

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

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225

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Springer Nature

Published Date

ISBN 10

303068928X

ISBN 13

9783030689285

Graph and Network Theory

An Applied Approach using Mathematica®

By: Michael A. Henning,Jan H. van Vuuren

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Graph and Network Theory

An Applied Approach using Mathematica®

By: Michael A. Henning,Jan H. van Vuuren

This textbook covers a diversity of topics in graph and network theory, both from a theoretical standpoint, and from an applied modelling point of view. Mathematica® is used to demonstrate much of the modelling aspects. Graph theory and model building tools are developed in tandem with effective techniques for solving practical problems via computer implementation. The book is designed with three primary readerships in mind. Individual syllabi or suggested sequences for study are provided for each of three student audiences: mathematics, applied mathematics/operations research, and computer science. In addition to the visual appeal of each page, the text contains an abundance of gems. Most chapters open with real-life problem descriptions which serve as motivation for the theoretical development of the subject matter. Each chapter concludes with three different sets of exercises. The first set of exercises are standard and geared toward the more mathematically inclined reader. Many of these are routine exercises, designed to test understanding of the material in the text, but some are more challenging. The second set of exercises is earmarked for the computer technologically savvy reader and offer computer exercises using Mathematica. The final set consists of larger projects aimed at equipping those readers with backgrounds in the applied sciences to apply the necessary skills learned in the chapter in the context of real-world problem solving. Additionally, each chapter offers biographical notes as well as pictures of graph theorists and mathematicians who have contributed significantly to the development of the results documented in the chapter. These notes are meant to bring the topics covered to life, allowing the reader to associate faces with some of the important discoveries and results presented. In total, approximately 100 biographical notes are presented throughout the book. The material in this book has been organized into three distinct parts, each with a different focus. The first part is devoted to topics in network optimization, with a focus on basic notions in algorithmic complexity and the computation of optimal paths, shortest spanning trees, maximum flows and minimum-cost flows in networks, as well as the solution of network location problems. The second part is devoted to a variety of classical problems in graph theory, including problems related to matchings, edge and vertex traversal, connectivity, planarity, edge and vertex coloring, and orientations of graphs. Finally, the focus in the third part is on modern areas of study in graph theory, covering graph domination, Ramsey theory, extremal graph theory, graph enumeration, and application of the probabilistic method.

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782

Publisher

Springer Nature

Published Date

ISBN 10

3031038576

ISBN 13

9783031038570

Book's by Michael N. Vrahatis

Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation

Pyomo — Optimization Modeling in Python

Pyomo — Optimization Modeling in Python

Graph and Network Theory

Graph and Network Theory

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