The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems
2003 ◽
Vol 125
(1)
◽
pp. 141-146
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Keyword(s):
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth or discontinuous problems with numerous local optima are considered, only the simulated annealing and the genetic algorithm, which are both characterized by a weak search heuristic, are successful in finding the optimal region in parameter space. The key advantage of the genetic algorithm is that it can easily be parallelized at negligible overhead.
2010 ◽
Vol 37-38
◽
pp. 203-206
1999 ◽
Vol 121
(2)
◽
pp. 249-252
◽
2017 ◽
Vol 1
(2)
◽
pp. 82
◽
2018 ◽
Vol 3
(4)
◽
pp. 365-380
◽
2014 ◽
Vol 3
(1)
◽
pp. 65-82
◽
2020 ◽
Vol 80
(5)
◽
pp. 910-931
2019 ◽
Vol 1237
◽
pp. 022137
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