- author = "Sin Man Cheang",
- title = "Genetic parallel programming",
- school = "The Chinese University of Hong Kong",
- year = "2005",
- address = "Hong Kong",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Applied sciences, Parallel computing, Sample weighting, Computer science",
- isbn13 = "9780542235290",
- language = "English",
-
URL = "
https://search.proquest.com/docview/305346245",
- size = "257 pages",
-
abstract = "This thesis investigates the design and implementation
of a novel linear-structured Genetic Programming (GP)
paradigm, Genetic Parallel Programming (GPP), in which
a parallel architecture, Multi-Arithmetic-Logic-Unit
Processor (MAP) is employed. The MAP is a MIMD,
general-purpose register machine that can be
implemented on modern Field Programmable Gate Arrays so
that genetic parallel programs can be evaluated at high
speed. Based on the parallel architecture of MAP, GPP
evolves genetic programs in parallel form. This thesis
presents a number of benchmark problems. The evolved
solution programs are precise and compact.
For human programmers, writing parallel programs is more difficult than writing sequential programs. However, an accelerating phenomenon in GPP, the GPP accelerating phenomenon, is observed. Experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. This creates a new approach to evolving a feasible problem solution program in parallel form and then serializes it into a sequential form if required. Since serialization is mechanical and its processing time is linear with respect to the size of the parallel program, the total learning time can be reduced significantly.
In order to evolve parallel programs effectively and efficiently, this thesis also investigates different genetic operators to assist the evolution. These operators include Dynamic Sample Weighting (DSW), dual-phase fitness functions and special types of mutation for parallel programs. Since the samples in a training set are captured directly from a real-world system, the distribution of these samples can be extremely biased. DSW adjusts the weights of training samples dynamically according to their past frequency of hits. Experimental results show that DSW boosts the evolutionary performance significantly...
To demonstrate the applicability of GPP, two application systems have been developed: (1) GPP Data Classification System (GPP-Classifier); and (2) GPP Logic Circuit Synthesizer (GPPLCS). The GPP-Classifier evolves MAP programs to classify data records in a database. The GPPLCS synthesizes combinational logic circuits directly from a truth table with different logic gates or RAM-based lookup-tables. High performance logic circuits are evolved and both their gate counts and propagation gate delays are less than that of the conventional designs...
... this thesis has made four major contributions: 1) parallel ; 2) revealing the GPP accelerating phenomenon; 3) inventing DSW 4) GPP Data Classification System a n d th e GPP Logic Circuit Synthesizer.",
-
notes = "ProQuest Dissertations and Theses 3182145.
Supervisors Kwong Sak Leung and Kin Hong Lee. proquest abstract was shorter than in pdf. Fibonacci",
