Advances and Trends in Genetic Programming

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. 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. Jasjit S. Suri is an innovator, scientist, visionary, industrialist and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his Ph.D. from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was crowned with President's Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management

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