Genetic Programming Theory and Practice VI
2009
https://doi.org/10.1007/978-0-387-87623-8…
14 pages
1 file
Sign up for access to the world's latest research
Abstract
• Papers developing techniques tested on small-scale problems include discussion of how to apply those techniques to real-world problems, while papers tackling real-world problems have employed techniques developed from theoretical work to gain insights.
Related papers
Journal of Intelligent and Robotic Systems, 2006
IC-AI, 2004
Genetic and evolutionary computation, 2018
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to engineering problem solving. First, the basic methodology is introduced. This is followed by a review of applications in the areas of systems modelling, control, optimisation and scheduling, design and signal processing. The paper concludes by suggesting potential avenues of research. *
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019
Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.
Genetic programming is a technique to automatically discover computer programs using principles of Darwinian evolution. This chapter introduces the basics of genetic programming. To make the material more suitable for beginners, these are illustrated with an extensive example. In addition, the chapter touches upon some of the more advanced variants of genetic programming as well as its theoretical foundations. Numerous pointers to further reading, software tools and Web sites are also provided.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
… , IEEE Transactions on, 1999
Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000
This research presents an evaluation of user defined domain specific functions of genetic programming using relational learning problems, generalisation for this class of learning problems and learning bias. After providing a brief theoretical background, two sets of experiments are detailed: experiments and results concerning the Monk-2 problem and experiments attempting to evolve generalising solutions to parity problems with incomplete data sets. The results suggest that using nonproblem specific functions may result in greater generalisation for relational problems.
XRDS: Crossroads, The ACM Magazine for Students, 2010
The user has requested enhancement of the downloaded file.

Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
References (2)
- O'Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors (2004). Genetic Programming Theory and Practice 11, volume 8 of Genetic Pro- gramming, Ann Arbor, MI, USA. Springer.
- Riolo, Rick L. and Worzel, Bill (2003). Genetic Programming Theory and Practice, volume 6 of Genetic Programming. Kluwer, Boston, MA, USA. Series Editor -John Koza.