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Advances and Trends in Genetic Programming, Volume One: Classification Techniques and Life Cycles presents the reader with complete coverage of the most current developments in Genetic Programming for Artificial Intelligence. The book provides a thorough look at classification as a systematic way of predicting class membership for a set of examples or instances using the properties of those examples. Classification arises in a wide variety of real life situations, such as detecting faces from large database, finding vehicles, matching fingerprints and diagnosing medical conditions.
A classification algorithm requires huge amount of accuracy and reliability that is very difficult for human programmers. Therefore, there is a need to develop an automated computer-based classification system that can classify the required objects.
- Presents the latest advances in Genetic Programming for Artificial Intelligence
- Discusses automated computer-based classification algorithms and systems, including comparison of different types of machine learning and two-class versus multi-class classification
- Includes discussions of tree-based Genetic Programming, the Intron problem, Dynamic Fitness Evaluation, Crossover and Mutation Operators, and a presentation of an integrated model-based Genetic Programming Algorithm for multi-class classification
Students and researchers in neural engineering and computer science who are interested in genetic programming solutions for a wide variety of applications
Section 1: Overview on Machine Learning
1. Introduction on Machine Learning, Genetic programming life cycles, and classification in multi class problems
2. Inter-comparison of different types of machine learning algorithm for classification
3. Two class versus multi-class classification for numeric data
4. Types of genetic programming and their applications
Section 2: Tree-Based Genetic Programming
5. Tree-based Genetic programming for Classification
6. Diversity in initial population of Genetic programming
7. Intron in Genetic programming
8. The problem of Bloat in Genetic Programming: Effects of bloat on the Classifier evolvement
Section 3: Crossover and Mutation Operators in Genetic Programming
9. Dynamic Fitness Evaluation: It’s effects on training paradigm
10. Crossover and Mutation Operators: How they Work in Parallel to Improve the Genetic Programming Life Cycle
11. An Integrated model-based Genetic Programming Algorithm for the Multi-class Classification
- No. of pages:
- © Academic Press 2021
- 1st March 2021
- Academic Press
- Paperback ISBN:
Harshit Bhardwaj did his M.Tech from Medicaps Institute of Science and Technology Indore, India in 2016. Currently, he is working as an Assistant Professor in Dronacharya Group of Institutions, Greater Noida, India. His research interests focus on Evolutionary Hybrid Algorithms. The motive behind this integration is to overcome individual limitations and achieve synergetic effects; more specifically these include Genetic Programming and Artificial Neural Networks and their applications in multi-class classification problems. In addition, he is also interested in Computer Vision. He has publications in Expert Systems with Application Elsevier Journal.
Assistant Professor, Dronacharya Group of Institutions, Greater Noida, India
Dr. Aruna Tiwari is an Associate Professor in Computer Science and Engineering at Indian Institute of Technology Indore (IIT Indore). She did her PhD in Computer Science & Engineering from RGPV Bhopal (MP). She did her M.E. and B.E. in Computer Engineering from Shri Govindaram Seksaria Institute Of Technology & Science, Indore (MP). Her research interests are around the Soft computing, Machine learning frameworks which can perform learning by handling real life ambiguous situations. Specifically, with artificial neural networks, fuzzy clustering, genetic programming and their applications to bioinformatics, medical diagnosis. The growing births of new intelligent system architectures are often due to the multi strategy learning and adaptation of advanced soft computing techniques in various fields such as pattern recognition, and data mining, particularly to address the issues of Big data for classification, clustering and feature selection. Big data computing needs advanced technologies or methods to solve the issues of computational time to extract valuable information, in a realistic and practical time frame, without compromising the model’s quality. Therefore, the need for developing intelligent scalable algorithms has been felt, which will be able to perform classification, clustering and feature selection in optimal sense after adjusting their parameters in an adaptive way to accomplish faster solutions to address Big data. Collaboration is enable with Soyabean Research Centre, Indore for testing real life big data. She has more than 50 publications in various transactions and journals. She is a life time member of Computer Society of India, IEEE Computational Intelligence Society, and Soft Computing Research Society, India.
Associate Professor in Computer Science and Engineering, Indian Institute of Technology Indore (IIT Indore)
Dr. Jasrit Suri, PhD, MBA, Fellow AIMBE is an innovator, visionary, scientist, and an internationally known world leader. Dr. Suri received the Director General’s Gold medal in 1980 and the Fellow of American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC in 2004. He has published over 650 peer reviewed articles and has over 100 innovations/trademarks. He has author/coauthored over 45 books. He is currently Chairman of Global Biomedical Technologies, Inc., Roseville, CA, and is on the board of AtheroPoint, Roseville, CA, a company dedicated to Atherosclerosis Imaging for early screening for stroke and cardiovascular monitoring. He has held positions as a chairman of IEEE Denver section and advisor board member to healthcare industries and several universities in USA and abroad.
Chairman of Global Biomedical Technologies, Inc.; Board Member, AtheroPoint, Roseville, CA, USA
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