Leandro Nunes de Castro

Bio: Leandro Nunes de Castro is an academic researcher from Mackenzie Presbyterian University. The author has contributed to research in topics: Cluster analysis & Artificial immune system. The author has an hindex of 30, co-authored 143 publications receiving 5248 citations. Previous affiliations of Leandro Nunes de Castro include State University of Campinas & Universidade Católica de Santos.

Artificial Immune Systems: A New Computational Intelligence Approach

Abstract: Introduction.- Fundamentals of the Immune System.- A Framework for Engineering Artificial Immune Systems.- A Survey of Artificial Immune Systems.- The Immune System in Context with Other Biological Systems.- AIS in Context with Other Computational Intelligence Paradigms.- Case Studies.- Conclusions and Future Trends.- References.- Appendix I: Glossary of Biological Terms.- Appendix II: Pseudocode for Immune Algorithms.- Appendix III: WEB Resources on Artificial Immune Systems. Index.

aiNet: An Artificial Immune Network for Data Analysis

Abstract: This chapter shows that some of the basic aspects of the natural immune system discussed in the previous chapter can be used to propose a novel artificial immune network model with the main goals of clustering and filtering crude data sets described by high-dimensional samples. Our aim is not to reproduce with confidence any immune phenomenon, but demonstrate that immune concepts can be used as inspiration to develop novel computational tools for data analysis. As important results of our model, the network evolved will be capable of reducing redundancy and describing data structure, including their spatial distribution and cluster interrelations. Clustering is useful in several exploratory pattern analyses, grouping, decision-making and machine-learning tasks, including data mining, knowledge discovery, document retrieval, image segmentation and automatic pattern classification. The data clustering approach was implemented in association with hierarchical clustering and graphtheoretical techniques, and the network performance is illustrated using several benchmark problems. The computational complexity of the algorithm and a detailed sensitivity analysis of the user-defined parameters are presented. A trade-off among the proposed model for data analysis, connectionist models (artificial neural networks) and evolutionary algorithms is also discussed.

Fundamentals Of Natural Computing: Basic Concepts, Algorithms, And Applications

Abstract: Introduction A Small Sample of Ideas The Philosophy of Natural Computing The Three Branches: A Brief Overview When to Use Natural Computing Approaches Conceptualization General Concepts PART I - COMPUTING INSPIRED BY NATURE Evolutionary Computing Problem Solving as a Search Task Hill Climbing and Simulated Annealing Evolutionary Biology Evolutionary Computing The Other Main Evolutionary Algorithms From Evolutionary Biology to Computing Scope of Evolutionary Computing Neurocomputing The Nervous System Artificial Neural Networks Typical ANNS and Learning Algorithms From Natural to Artificial Neural Networks Scope of Neurocomputing Swarm Intelligence Ant Colonies Swarm Robotics Social Adaptation of Knowledge Immunocomputing The Immune System Artificial Immune Systems Bone Marrow Models Negative Selection Algorithms Clonal Selection and Affinity Maturation Artificial Immune Networks From Natural to Artificial Immune Systems Scope of Artificial Immune Systems PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS Fractal Geometry of Nature The Fractal Geometry of Nature Cellular Automata L-Systems Iterated Function Systems Fractional Brownian Motion Particle Systems Evolving the Geometry of Nature From Natural to Fractal Geometry Artificial Life Concepts and Features of Artificial Life Systems Examples of Artificial Life Projects Scope of Artificial Life From Artificial Life to Life-As-We-Know-It PART III - COMPUTING WITH NATURAL MATERIALS DNA Computing Basic Concepts from Molecular Biology Filtering Models Formal Models: A Brief Description Universal DNA Computers Scope of DNA Computing From Classical to DNA Computing Quantum Computing Basic Concepts from Quantum Theory Principles from Quantum Mechanics Quantum Information Universal Quantum Computers Quantum Algorithms Physical Realizations of Quantum Computers: A Brief Description Scope of Quantum Computing From Classical to Quantum Computing Afterwords New Prospects The Growth of Natural Computing Some Lessons from Natural Computing Artificial Intelligence and Natural Computing Visions Appendix A: Glossary of Terms Appendix B: Theoretical Background Linear Algebra Statistics Theory of Computation and Complexity Other Concepts Bibliography Appendix C: A Quick Guide to the Literature Introduction Conceptualization Evolutionary Computing Neurocomputing Swarm Intelligence Immunocomputing Fractal Geometry of Nature Artificial Life DNA Computing Quantum Computing Index *All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Recent Developments In Biologically Inspired Computing

Abstract: Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem-solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and swarm systems. It also brings together novel and philosophical trends in the exciting fields of artificial life and robotics. This book has the advantage of covering a large number of computational approaches, presenting the state-of-the-art before entering into the details of specific extensions and new developments. Pseudocodes, flow charts and examples of applications are provided of the new approaches presented.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).