14,266 research outputs found
Constructing multiple unique input/output sequences using metaheuristic optimisation techniques
Multiple unique input/output sequences (UIOs) are often used to generate robust and compact test sequences in finite state machine (FSM) based testing. However, computing UIOs is NP-hard. Metaheuristic optimisation techniques (MOTs) such as genetic algorithms (GAs) and simulated annealing (SA) are effective in providing good solutions for some NP-hard problems. In the paper, the authors investigate the construction of UIOs by using MOTs. They define a fitness function to guide the search for potential UIOs and use sharing techniques to encourage MOTs to locate UIOs that are calculated as local optima in a search domain. They also compare the performance of GA and SA for UIO construction. Experimental results suggest that, after using a sharing technique, both GA and SA can find a majority of UIOs from the models under test
Solving the undirected feedback vertex set problem by local search
An undirected graph consists of a set of vertices and a set of undirected
edges between vertices. Such a graph may contain an abundant number of cycles,
then a feedback vertex set (FVS) is a set of vertices intersecting with each of
these cycles. Constructing a FVS of cardinality approaching the global minimum
value is a optimization problem in the nondeterministic polynomial-complete
complexity class, therefore it might be extremely difficult for some large
graph instances. In this paper we develop a simulated annealing local search
algorithm for the undirected FVS problem. By defining an order for the vertices
outside the FVS, we replace the global cycle constraints by a set of local
vertex constraints on this order. Under these local constraints the cardinality
of the focal FVS is then gradually reduced by the simulated annealing dynamical
process. We test this heuristic algorithm on large instances of Er\"odos-Renyi
random graph and regular random graph, and find that this algorithm is
comparable in performance to the belief propagation-guided decimation
algorithm.Comment: 6 page
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A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs
Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the NelderâMead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs
Molecular geometry optimization with a genetic algorithm
We present a method for reliably determining the lowest energy structure of
an atomic cluster in an arbitrary model potential. The method is based on a
genetic algorithm, which operates on a population of candidate structures to
produce new candidates with lower energies. Our method dramatically outperforms
simulated annealing, which we demonstrate by applying the genetic algorithm to
a tight-binding model potential for carbon. With this potential, the algorithm
efficiently finds fullerene cluster structures up to starting
from random atomic coordinates.Comment: 4 pages REVTeX 3.0 plus 3 postscript figures; to appear in Physical
Review Letters. Additional information available under "genetic algorithms"
at http://www.public.iastate.edu/~deaven
Stochastic optimization methods for extracting cosmological parameters from CMBR power spectra
The reconstruction of the CMBR power spectrum from a map represents a major
computational challenge to which much effort has been applied. However, once
the power spectrum has been recovered there still remains the problem of
extracting cosmological parameters from it. Doing this involves optimizing a
complicated function in a many dimensional parameter space. Therefore efficient
algorithms are necessary in order to make this feasible. We have tested several
different types of algorithms and found that the technique known as simulated
annealing is very effective for this purpose. It is shown that simulated
annealing is able to extract the correct cosmological parameters from a set of
simulated power spectra, but even with such fast optimization algorithms, a
substantial computational effort is needed.Comment: 7 pages revtex, 3 figures, to appear in PR
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