Arpit Bhardwaj, Aruna Tiwari, Jasjit S Suri

Advances and Trends in Genetic Programming

Volume 1: Classification Techniques and Life Cycles

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 220
• Erscheinungstermin: 1. März 2022
• Englisch
• Abmessung: 235mm x 191mm
• ISBN-13: 9780128180204
• ISBN-10: 012818020X
• Artikelnr.: 59468948

Autorenporträt

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.

Inhaltsangabe

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