Genetic Parallel Programming: design and implementation.
This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
Cheang
Sin Man
SM
Department of Computing, Hong Kong Institute of Vocational Education (Kwai Chung Campus), 20 Hing Shing Road, Kwai Chung, Hong Kong. smcheang@vtc.edu.hk
Leung
Kwong Sak
KS
Lee
Kin Hong
KH
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003205:Computing Methodologies; D019143:Evolution, Molecular; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D012984:Software