%0 Conference Paper %1 cheang:2003:CIRAS %A Cheang, Sin Man %B Proceedings of the second International Conference on Computational Intelligence, Robotics and Autonomous Systems -- CIRAS-2003 %C Singapore %D 2003 %E Vadakkepat, P. %E Wan, T. W. %E Chen, T. K. %E Poh, L. A. %I National Univ. of Singapore %K algorithms, genetic programming %P PS04--4--03 %T An Empirical Study of the GPP Accelerating Phenomenon %X The Genetic Parallel Programming (GPP) is a novel Linear-structure Genetic Programming paradigm that learns parallel programs. We discover the GPP Accelerating Phenomenon, i.e. parallel programs are evolved faster than their counterpart sequential programs of identical functions. This paper presents an empirical study of Boolean function regression based on a Multi-ALU Processor that results in the phenomenon. We performed a series of random search experiments using different numbers of ALUs (w) and instructions (l). We identify that w (the degree of parallelism of the program) is the dominant factor that affects the searching performance. In a 3-input Boolean function experiment, searching a single-ALU program requires 875 times on average of the computational effort of an 8-ALU program. An investigation on the probabilities of finding solutions to different problem instances shows that parallel representation of programs can increase the probabilities of finding solutions to hard problems.

@inproceedings{cheang:2003:CIRAS, abstract = {The Genetic Parallel Programming (GPP) is a novel Linear-structure Genetic Programming paradigm that learns parallel programs. We discover the GPP Accelerating Phenomenon, i.e. parallel programs are evolved faster than their counterpart sequential programs of identical functions. This paper presents an empirical study of Boolean function regression based on a Multi-ALU Processor that results in the phenomenon. We performed a series of random search experiments using different numbers of ALUs (w) and instructions (l). We identify that w (the degree of parallelism of the program) is the dominant factor that affects the searching performance. In a 3-input Boolean function experiment, searching a single-ALU program requires 875 times on average of the computational effort of an 8-ALU program. An investigation on the probabilities of finding solutions to different problem instances shows that parallel representation of programs can increase the probabilities of finding solutions to hard problems.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Singapore}, author = {Cheang, Sin Man}, biburl = {https://www.bibsonomy.org/bibtex/235916b29f72ad80bd61ea6e1893826d3/brazovayeye}, booktitle = {Proceedings of the second International Conference on Computational Intelligence, Robotics and Autonomous Systems -- CIRAS-2003}, editor = {Vadakkepat, P. and Wan, T. W. and Chen, T. K. and Poh, L. A.}, interhash = {7c98b7ac40cfa43c4b839628adae3e64}, intrahash = {35916b29f72ad80bd61ea6e1893826d3}, keywords = {algorithms, genetic programming}, month = {15-18 December}, organisation = {Centre for Intelligent Control, National Univ. of Singapore}, pages = {PS04--4--03}, publisher = {National Univ. of Singapore}, timestamp = {2008-06-19T17:37:36.000+0200}, title = {An Empirical Study of the {GPP} Accelerating Phenomenon}, year = 2003 }