The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Genetic Programming Using a Turing-Complete Representation: Recurrent Network Consisting of Trees
Abstract
In this chapter, a new representation scheme for Genetic Programming (GP) is proposed. We need a Turing-complete representation for a general method of generating programs automatically; that is, the representation must be able to express any algorithms. Our representation is a recurrent network consisting of trees (RTN), which is proved to be Turing-complete. In addition, it is applied to the tasks of generating language classifiers and a bit reverser. As a result, RTN is shown to be usable in evolutionary computing.
Related Content
|
Rahul Ratnakumar, Shilpa K., Satyasai Jagannath Nanda.
© 2023.
27 pages.
|
|
Parth Birthare, Maheswari Raja, Ganesan Ramachandran, Carol Anne Hargreaves, Shreya Birthare.
© 2023.
29 pages.
|
|
Raja G., Srinivasulu Reddy U..
© 2023.
22 pages.
|
|
Maheswari R., Pattabiraman Venkatasubbu, A. Saleem Raja.
© 2023.
19 pages.
|
|
Maheswari R., Prasanna Sundar Rao, Azath H., Vijanth S. Asirvadam.
© 2023.
26 pages.
|
|
Gayathri S. P., Siva Shankar Ramasamy, Vijayalakshmi S..
© 2023.
22 pages.
|
|
Chitra P..
© 2023.
15 pages.
|
|
|