Multi-objective optimization of QCA circuits with multiple outputs using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8194
- @Article{Rezaee:2013:GPEM,
-
author = "Razieh Rezaee and Mahboobeh Houshmand and
Monireh Houshmand",
-
title = "Multi-objective optimization of {QCA} circuits with
multiple outputs using genetic programming",
-
journal = "Genetic Programming and Evolvable Machines",
-
year = "2013",
-
volume = "14",
-
number = "1",
-
pages = "95--118",
-
month = mar,
-
keywords = "genetic algorithms, genetic programming, Hardware
reduction, Delay reduction, Majority expression,
Multi-output circuits, Multi-objective GP",
-
ISSN = "1389-2576",
-
DOI = "doi:10.1007/s10710-012-9173-6",
-
size = "24 pages",
-
abstract = "Quantum-Dot Cellular Automata (QCA) is a promising
nanotechnology that has been recognised as one of the
top emerging technologies in future computers. Size
density of several orders of magnitude smaller than
Complementary Metal-Oxide Semiconductor, fast switching
time and extremely low power, has caused QCA to become
a topic of intense research. The majority gate and the
inverter gate together make a universal set of Boolean
primitives in QCA technology. Reducing the number of
required primitives to implement a given Boolean
function is an important step in designing QCA logic
circuits. Previous research has shown how to use
genetic programming to minimise the number of gates
implementing a given Boolean function with one output.
In this paper, we first show how to minimize the gates
for the given Boolean truth tables with an arbitrary
number of outputs using genetic programming. Then,
another criterion, reduction of the delay of the
implementing circuit is considered. Multi-objective
genetic programming is applied to simultaneously
optimise both objectives. The results demonstrate the
proposed approach is promising and worthy of further
research.",
-
notes = "Karnaugh maps, clock, multi-objective, Pareto,
double-point crossover, multiple output linked trees
(DAG), pop 100, 5000 generations, all 3 input Boolean
problems, 15percent of all 4 input Boolean problems,
roulette wheel selection, minterms.
Cites \cite{DBLP:conf/dac/AntonelliCDHKKMN04},
\cite{Bonyadi:2007:ICEE}, \cite{Houshmand:2009:ICIS},
\cite{Rajaei:2011:SCIA}.",
-
affiliation = "Department of Computer Engineering, Ferdowsi
University of Mashhad, Mashhad, Iran",
- }
Genetic Programming entries for
Razieh Rezaee
Mahboobeh Houshmand
Monireh Houshmand
Citations