Introduction to Computational Intelligence
An IEEE Computational Intelligence Society Open Book
Edited by Leandro L. Minku, George G. Cabral, Marcella Martins and Markus Wagner
Link to the first edition (first revision) book -- download the files and open the main.pdf file: https://doi.org/10.5281/zenodo.7537827
Due to its power to solve large scale problems and analyse vast amounts of data that would be difficult or time consuming for humans to deal with manually, the field of Computational Intelligence has grown tremendously in importance over the past years. We nowadays see widespread use of computational intelligence in the most varied applications. A few examples include approaches to detect credit card fraud, recognise faces, transcribe voice to text, identify spam, route and schedule deliveries, design aerodynamic high speed trains, etc.
It is thus not surprising that we see a growing number of people who are keen to learn about this field. However, there is a lack of open resources that combine several different types of computational intelligence approaches in one place, so that people can easily get an introduction to this field. Those eager to learn about computational intelligence may also struggle to get help from others when trying to understand existing approaches, whereas those willing to start teaching this topic may struggle to find free resources to guide them.
This open book has been created as a community effort to overcome these issues. The notion of \textit{openness} of this book includes, but goes beyond open access. In addition to being available through an open license so that resources on computational intelligence are accessible to all, this book is hosted in github at:
https://github.com/ieee-cis/IEEE-CIS-Open-Access-Book-Volume-1
Such initiative will enable the book to be continuously improved over time through pull requests to fix typos, add clarifications, add new exercises, add examples of open software code, add video lectures on the content, etc. Therefore, this book is \textit{open} for the community to propose enhancements over time. The book is also associated to github discussion boards, so that people can ask questions and the community can help with answering those questions, creating an \textit{open} community that all can join in. The discussion boards can be found in the github repository at:
https://github.com/ieee-cis/IEEE-CIS-Open-Access-Book-Volume-1/discussions
If you would like to propose an enhancement through a pull request to this book, we ask you to first contact the current chair of the IEEE Computational Intelligence Society Education Portal Subcommittee (\url{https://cis.ieee.org/}). The chair will advise you on how to proceed. Minor changes to existing chapters will be handled by the subcommittee directly, whereas the subcommittee will liaise with the original authors to obtain their consent for incorporating larger pull requests.
We thank all the authors who have contributed chapters to this book, all the anonymous reviewers who have reviewed the chapters, and all the community members who will contribute with this book and discussion boards in the future.
We hope that you will find the book a useful resource to learn about computational intelligence.
Table of Contents
- Part I Introduction
- Chapter 1: History and Definitions of Computational Intelligence by Leandro L. Minku
- Part II Search-Based Optimization
- Chapter 2: Introduction to Search-Based Optimization by Leandro L. Minku
- Chapter 3: Local Search by Sara Ceschia, Luca Di Gaspero, Andrea Schaerf
- Chapter 4: Simulated Annealing by Alberto Franzin, Thomas Stutzle
- Chapter 5: Particle Swarm Optimization by Diego Oliva, Alfonso Ramos-Michel, Mario A. Navarro, Eduardo H. Haro, Angel Casas
- Chapter 6: Other Search-Based Optimization Approaches by Roberto Santana
- Part III Learning Systems
- Chapter 7: Introduction to Learning Systems by Amanda Cristina Fraga De Albuquerque Brendon Erick Euzebio Rus Peres Erikson Freitas de Morais Gilson Junior Soares Jose Lohame Capinga Marcella Scoczynski Ribeiro Martins
- Part III-(A) Supervised Learning
- Chapter 8: k-Nearest Neighbors by George Cabral
- Chapter 9: Multilayer Perceptron by Lucas Costa, Marcio Guerreiro, Erickson Puchta, Yara de Souza Tadano, Thiago Antonini Alves, Mauricio Kaster, and Hugo Valadares Siqueira
- Chapter 10: Deep Learning by Amanda Cristina Fraga De Albuquerque, Brendon Erick Euzebio Rus Peres, Erikson Freitas de Morais, Gilson Junior Soares, Jose Lohame Capinga, and Marcella Scoczynski Ribeiro Martins
- Chapter 11: Naive Bayes by Leandro L. Minku
- Chapter 12: Evaluation of Supervised Learning Models by George G. Cabral and Leandro L. Minku
- Part III-(B) Unsupervised Learning
- Chapter 13: k-Means by Harish Tayyar Madabushi
- Chapter 14: Hierarchical Clustering by Shuo Wang
- Chapter 15: DBScan by Lina Yao
- Chapter 16: Expectation Maximization by Bruno Almeida Pimentel
- Chapter 17: Self Organizing Maps by George G. Cabral
- Chapter 18: Clustering Evaluation by Valmir Macario