- author = "A. M. S. Zalzala and D. Green",
- title = "{MTGP}: A Multithreaded Java Tool for Genetic Programming Applications",
- booktitle = "Proceedings of the Congress on Evolutionary Computation",
- year = "1999",
- editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
- volume = "2",
- pages = "904--912",
- address = "Mayflower Hotel, Washington D.C., USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, algorithms, Java applet, MTGP, evolutionary performance, multithreaded Java tool, Java, multi-threading, software tools",
- ISBN = "0-7803-5536-9 (softbound)",
- ISBN = "0-7803-5537-7 (Microfiche)",
-
URL = "
http://deron.csie.ncue.edu.tw/AI/paper/MTGP%20a%20multithreaded%20Java%20tool%20for%20genetic%20programming%20applications.pdf",
-
DOI = "
doi:10.1109/CEC.1999.782519",
- size = "9 pages",
- abstract = "MTGP is a new genetic programming system that uses the multithreading technology of the Java programming language for the parallel evolution of subpopulations of programs. The system runs as a Java applet within a standard web browser on a desktop PC, and uses a linear program representation for a stack-based virtual machine. The individuals from four subpopulations are manipulated concurrently and these subpopulations exchange their best individuals at regular intervals during a run. MTGP incorporates novel variations on the traditional genetic operators used in genetic programming and in the inclusion of a 'do nothing' gene, in an attempt to produce better evolutionary performance. The basic procedures of the system will be used in the future development of a distributed, Internet-based genetic programming system that will provide large computational power needed to solve complex problems. In this report, the performance of MTGP on two symbolic regression problems is compared to that of four other genetic programming systems. MTGP shows improvement over these systems in terms of the computational effort needed to solve the problems and the accuracy of the solution produced.",
-
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
