Seminar Report – Application Of Genetic Programming To Classification

Genetic Programming Seminar Abstract

Classification is one of the most researched questions in machine learning and data mining. Genetic programming is a recent development in the area of evolutionary computation. It was greatly stimulated in the 1990s by John Koza. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. Flexibility is one of the main advantages of GP, and this feature allows GP to be applied for classification in many different ways. One of the central problems in computer science is how to make computers solve problems without being explicitly programmed to do so. Genetic programming offers a solution through the evolution of computer programs by methods of natural selection.According to Koza, genetic programming searches the space of possible computer programs that are highly fit for solving the problem at hand. This paper surveys about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers

View Full Report

You may also like these posts

Latest IEEE Electronics Seminars Topics with Full Reports- 2017

New Electrical Seminar Topics – 2017

if you find any broken links on this page, please report to us. We will fix it for you 🙂

Leave a Reply

Your email address will not be published. Required fields are marked *