%0 Conference Paper %1 Leung:2002:ICAIET %A Leung, Kwong Sak %A Lee, Kin Hong %A Cheang, Sin Man %B Proceedings of International Conference on Artificial Intelligence in Engineering and Technology - ICAIET 2002 %D 2002 %E Yaacob, Sazali %E Nagarajan, R. %E Chekima, Ali %I Universiti Malaysia Sabah %K algorithms, genetic programming %P 207--213 %T Genetic Parallel Programming - Evolving Linear Machine Codes on a Multiple-ALU Processor %X Genetic Programming (GP) is a robust method in Evolutionary Computation. There are two main streams in GP, namely, Tree-based GP (TGP) and Linear GP (LGP). TGP evolves programs represented in tree structure. LGP evolves sequential programs directly. LGP suffers from inflexibility while TGP suffers from inefficiency. This paper proposes a novel framework of an integrated system called Genetic Parallel Programming (GPP) for evolving optimal parallel programs by LGP. The core of the GPP consists of a Multi-ALU Processor (MAP) and an Evolution Engine (EE). The MAP uses Multiple Instruction streams Multiple Data streams (MIMD) architecture. The EE uses a two-phase evolutionary approach and a new GP operation to swap sub-instructions in a parallel program. Three experiments (i.e. Cubic function, Sextic function and Artificial Ant - Santa Fe Trail) are given as examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism. The GPP opens up an entire new opportunity for solving problems with appropriate parallel architecture and learning optimal programs/algorithms automatically. %@ 983-2188-92-X

@inproceedings{Leung:2002:ICAIET, abstract = {Genetic Programming (GP) is a robust method in Evolutionary Computation. There are two main streams in GP, namely, Tree-based GP (TGP) and Linear GP (LGP). TGP evolves programs represented in tree structure. LGP evolves sequential programs directly. LGP suffers from inflexibility while TGP suffers from inefficiency. This paper proposes a novel framework of an integrated system called Genetic Parallel Programming (GPP) for evolving optimal parallel programs by LGP. The core of the GPP consists of a Multi-ALU Processor (MAP) and an Evolution Engine (EE). The MAP uses Multiple Instruction streams Multiple Data streams (MIMD) architecture. The EE uses a two-phase evolutionary approach and a new GP operation to swap sub-instructions in a parallel program. Three experiments (i.e. Cubic function, Sextic function and Artificial Ant - Santa Fe Trail) are given as examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism. The GPP opens up an entire new opportunity for solving problems with appropriate parallel architecture and learning optimal programs/algorithms automatically.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Leung, Kwong Sak and Lee, Kin Hong and Cheang, Sin Man}, biburl = {https://www.bibsonomy.org/bibtex/219d1b41de9f6732a1010649fa0d0b529/brazovayeye}, booktitle = {Proceedings of International Conference on Artificial Intelligence in Engineering and Technology - ICAIET 2002}, editor = {Yaacob, Sazali and Nagarajan, R. and Chekima, Ali}, interhash = {c5ae648025f27376c9fc93e2b86fed78}, intrahash = {19d1b41de9f6732a1010649fa0d0b529}, isbn = {983-2188-92-X}, keywords = {algorithms, genetic programming}, month = {June}, organisation = {School of Engineering and Information Technology, Universiti Malaysia Sabah}, pages = {207--213}, publisher = {Universiti Malaysia Sabah}, timestamp = {2008-06-19T17:45:26.000+0200}, title = {Genetic Parallel Programming - Evolving Linear Machine Codes on a Multiple-{ALU} Processor}, year = 2002 }