@inproceedings{zalzala:1999:MAMJTGPA,
	title = {{MTGP}: {A} Multithreaded Java Tool for Genetic
                 Programming Applications},
	address = {Mayflower Hotel, Washington D.C., USA},
	author = {A. M. S. Zalzala and D. Green},
	booktitle = {Proceedings of the Congress on Evolutionary
                 Computation},
	editor = {Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala},
	month = {6-9 July},
	pages = {904--912},
	publisher = {IEEE Press},
	url = {http://deron.csie.ncue.edu.tw/AI/paper/MTGP%20a%20multithreaded%20Java%20tool%20for%20genetic%20programming%20applications.pdf},
	volume = {2},
	year = {1999},
	biburl = {http://www.bibsonomy.org/bibtex/2a8798701e2fba9ac2d3ac56ff0a9254a/brazovayeye},
	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.},
	organisation = {Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA}, size = {9 pages}, isbn = {0-7803-5537-7 (Microfiche)}, notes = {CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143},
	keywords = {algorithms algorithms, genetic programming, }
}