we present three meta-heuristic approaches for FPGA
segmented channel routing problems (FSCRPs) with a new
cost function in which the cost of each assignment is
not known in advance, and the cost of a solution only
can be obtained from entire feasible assignments.
Previous approaches to FSCPs cannot be applied to this
kind of cost functions, and meta-heuristics are a good
option to tackle the problem. We present two hybrid
algorithms which use a Hopfield neural network to solve
the problem's constraints, mixed with a Genetic
Algorithm (GA) and a Simulated Annealing (SA). The
third approach is a GA which manages the problem's
constraints with a penalty function. We provide a
complete analysis of the three metaheuristics, by
tested them in several FSCRP instances, and comparing
their performance and suitability to solve the FSCRP.
%0 Journal Article
%1 Salcedo-Sanz:2005:GPEM
%A Salcedo-Sanz, Sancho
%A Xu, Yong
%A Yao, Xin
%D 2005
%J Genetic Programming and Evolvable Machines
%K FPGAs, algorithms, annealing architecture, channel evolvable genetic hardware, hybrid segmented simulated
%N 4
%P 359--379
%R doi:10.1007/s10710-005-3295-z
%T Meta-Heuristic Algorithms for FPGA Segmented Channel
Routing Problems with Non-standard Cost Functions
%V 6
%X we present three meta-heuristic approaches for FPGA
segmented channel routing problems (FSCRPs) with a new
cost function in which the cost of each assignment is
not known in advance, and the cost of a solution only
can be obtained from entire feasible assignments.
Previous approaches to FSCPs cannot be applied to this
kind of cost functions, and meta-heuristics are a good
option to tackle the problem. We present two hybrid
algorithms which use a Hopfield neural network to solve
the problem's constraints, mixed with a Genetic
Algorithm (GA) and a Simulated Annealing (SA). The
third approach is a GA which manages the problem's
constraints with a penalty function. We provide a
complete analysis of the three metaheuristics, by
tested them in several FSCRP instances, and comparing
their performance and suitability to solve the FSCRP.
@article{Salcedo-Sanz:2005:GPEM,
abstract = {we present three meta-heuristic approaches for FPGA
segmented channel routing problems (FSCRPs) with a new
cost function in which the cost of each assignment is
not known in advance, and the cost of a solution only
can be obtained from entire feasible assignments.
Previous approaches to FSCPs cannot be applied to this
kind of cost functions, and meta-heuristics are a good
option to tackle the problem. We present two hybrid
algorithms which use a Hopfield neural network to solve
the problem's constraints, mixed with a Genetic
Algorithm (GA) and a Simulated Annealing (SA). The
third approach is a GA which manages the problem's
constraints with a penalty function. We provide a
complete analysis of the three metaheuristics, by
tested them in several FSCRP instances, and comparing
their performance and suitability to solve the FSCRP.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Salcedo-Sanz, Sancho and Xu, Yong and Yao, Xin},
biburl = {https://www.bibsonomy.org/bibtex/2f2d0f72766685f051d19c41f65ab2c4f/brazovayeye},
doi = {doi:10.1007/s10710-005-3295-z},
interhash = {a627541a72f39ddb351db9417cda6cf1},
intrahash = {f2d0f72766685f051d19c41f65ab2c4f},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {FPGAs, algorithms, annealing architecture, channel evolvable genetic hardware, hybrid segmented simulated},
month = {December},
number = 4,
pages = {359--379},
size = {21 pages},
timestamp = {2008-06-19T17:50:56.000+0200},
title = {Meta-Heuristic Algorithms for {FPGA} Segmented Channel
Routing Problems with Non-standard Cost Functions},
volume = 6,
year = 2005
}