id: 02159393
dt: a
an: 02159393
au: Moscato, Pablo; Mendes, Alexandre; Linhares, Alexandre
ti: VLSI design: gate matrix layout problem.
so: Onwubolu, Godfrey C. (ed.) et al., New optimization techniques in
    engineering. Berlin: Springer (ISBN 3-540-20167-X/hbk). Studies in
    Fuzziness and Soft Computing 141, 455-478 (2004).
py: 2004
pu: Berlin: Springer
la: EN
cc: 
ut: fuzzy modeling; multicriteria optimization; networks; memetic algorithm
ci: Zbl 1016.93041; Zbl 1002.93536; Zbl 1005.68132; Zbl 0844.90079
li: 
ab: From the introduction: With applications ranging from fields as distinct as
    fuzzy modeling [{\it N. Xiong}, Int. J. Syst. Sci. 32, No.~9,
    1109‒1118 (2001; Zbl 1016.93041)], autonomous robot behavior [{\it B.
    L. Luk, S. Galt} and {\it S. Chen}, Int. J. Syst. Sci. 32, No.~6,
    703‒713 (2001; Zbl 1002.93536)], learning with backpropagation [{\it
    S. K. Foo, P. Saratchandran} and {\it N. Sundararajan}, Int. J. Syst.
    Sci. 30, No.~3, 309‒321 (1999; Zbl 1005.68132)], and multicriteria
    optimization [{\it R. Viennet, C. Fonteix} and {\it I. Marc}, Int. J.
    Syst. Sci. 27, No.~2, 255‒260 (1996; Zbl 0844.90079)], evolutionary
    methods have become an indispensable tool for systems scientists.
    Although already studied in the past, an interesting emerging issue is
    the use of multiple populations, which is gaining increased momentum
    from the conjunction of two technologies. On the hardware side,
    computer networks, multi-processor computers and distributed processing
    systems (such as workstations clusters) are increasingly becoming
    widespread. Regarding the software issues, the introduction of parallel
    virtual machine, and later the message passing interface standard, as
    well as web-enabled, object-oriented languages (such as Java) have also
    had their role. Most Evolutionary Algorithms (EAs) are methods that are
    easy to parallelize as well as naturally suitable for heterogeneous
    systems. For most EAs the distribution of the tasks is relatively easy
    for most applications. The workload can be distributed at an individual
    or a population level, the final choice depending on the complexity of
    the computations involved.  The application presented in this chapter
    is motivated by the availability of these computer environments.
    However, here we do not report results on the use of parallel
    computers, or networks of workstations. The proposed memetic algorithm
    runs in a sequential way on a single processor, but a set of
    populations evolve separately, an approach that can be easily mapped to
    a parallel environment
rv: