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.
The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems
Contributed by the Bioengineering Division for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received Jul. 2000; revised manuscript received Sept. 2002. Associated Editor: M. G. Pandy.
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van Soest , A. J. K., and Casius , L. J. R. R. (February 14, 2003). "The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems ." ASME. J Biomech Eng. February 2003; 125(1): 141–146. https://doi.org/10.1115/1.1537735
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