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R 916.00
Evolutionary Algorithms
This IMA Volume in Mathematics and its Applications EVOLUTIONARY ALGORITHMS is based on the proceedings of a workshop that was an integral part of the 1996-97 IMA program on "MATHEMATICS IN HIGH-PERFORMANCE COMPUTING." I thank Lawrence David Davis (Tica Associates), Kenneth De Jong (Computer Science, George Mason University), Michael D. Vose (Computer Science, The University of Tennessee), and L. Darrell Whitley (Computer Science, Colorado State University) for their excellent work in organizing the workshop and for editing the proceedings. Further appreciation is ex tended to Donald G. Truhlar (Chemistry and Supercomputing Institute, University of Minnesota) who was also one of the workshop organizers. In addition, I also take this opportunity to thank the National Science Foundation (NSF), Minnesota Supercomputing Institute (MSI), and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr., Professor and Director v PREFACE The IMA Workshop on Evolutionary Algorithms brought together many of the top researchers working in the area of Evolutionary Com putation for a week of intensive interaction. The field of Evolutionary Computation has developed significantly over the past 30 years and today consists a variety of subfields such as genetic algorithms, evolution strate gies, evolutionary programming, and genetic programming, each with their own algorithmic perspectives and goals.
R 1,559.31
R 4,502.00
Automated Design of Machine Learning and Search Algorithms
This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
R 4,809.94
Metaheuristics Algorithms for Medical Applications
Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.
R 5,297.00
Automated Design of Machine Learning and Search Algorithms
This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
R 5,047.11
Evolutionary and Swarm Intelligence Algorithms
This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
R 6,592.24
Advances in Metaheuristics Algorithms: Methods and Applications
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
R 4,757.00
Stable Mutations for Evolutionary Algorithms
This book presents a set of theoretical and experimental results that describe the features of the wide family of α-stable distributions (the normal distribution also belongs to this class) and their various applications in the mutation operator of evolutionary algorithms based on real-number representation of the individuals, and, above all, equip these algorithms with features that enrich their effectiveness in solving multi-modal, multi-dimensional global optimization problems. The overall conclusion of the research presented is that the appropriate choice of probabilistic model of the mutation operator for an optimization problem is crucial.Mutation is one of the most important operations in stochastic global optimization algorithms in the n-dimensional real space. It determines the method of search space exploration and exploitation. Most applications of these algorithms employ the normal mutation as a mutation operator. This choice is justified by the central limit theorem but is associated with a set of important limitations. Application of α-stable distributions allows more flexible evolutionary models to be obtained than those with the normal distribution. The book presents theoretical analysis and simulation experiments, which were selected and constructed to expose the most important features of the examined mutation techniques based on α-stable distributions. It allows readers to develop a deeper understanding of evolutionary processes with stable mutations and encourages them to apply these techniques to real-world engineering problems.
R 4,030.00
Cultural Algorithms
A thorough look at how societies can use cultural algorithms to understand human social evolution For those working in computational intelligence, developing an understanding of how cultural algorithms and social intelligence form the essential framework for the evolution of human social interaction is essential. This book, Cultural Algorithms: Tools to Model Complex Dynamic Social Systems, is the foundation of that study. It showcases how we can use cultural algorithms to organize social structures and develop socio-political systems that work. For such a vast topic, the text covers everything from the history of the development of cultural algorithms and the basic framework with which it was organized. Readers will also learn how other nature-inspired algorithms can be expressed and how to use social metrics to assess the performance of various algorithms. In addition to these topics, the book covers topics including: The CAT system including the Repast Simphony System and CAT Sample RunsHow to problem solve using social networks in cultural algorithms with auctionsUnderstanding Common Value Action to enhance Social Knowledge Distribution SystemsCase studies on team formationsAn exploration of virtual worlds using cultural algorithms For industry professionals or new students, Cultural Algorithms provides an impactful and thorough look at both social intelligence and how human social evolution translates into the modern world.
R 4,557.00
Dependency Structures and Lexicalized Grammars
Since 2002, FoLLI has awarded an annual prize for outstanding dissertations in the fields of Logic, Language and Information. This book is based on the PhD thesis of Marco Kuhlmann, joint winner of the E.W. Beth dissertation award in 2008.Kuhlmann’s thesis lays new theoretical foundations for the study of non-projective dependency grammars. These grammars are becoming increasingly important for approaches to statistical parsing in computational linguistics that deal with free word order and long-distance dependencies. The author provides new formal tools to define and understand dependency grammars, presents two new dependency language hierarchies with polynomial parsing algorithms, establishes the practical significance of these hierarchies through corpus studies, and links his work to the phrase-structure grammar tradition through an equivalence result with tree-adjoining grammars. The work bridges the gaps between linguistics and theoretical computer science, between theoretical and empirical approaches in computational linguistics, and between previously disconnected strands of formal language research.
R 1,532.13
