The merits of a parallel genetic algorithm in solving hard optimization problems.
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.
van Soest
A J Knoek
AJ
Faculty of Human Movement Sciences, Institute for Fundamental and Clinical Human Movement Sciences, Free University Amsterdam, van der Boechorststraat 9, NL 1081 Amsterdam, The Netherlands. a_j_van_soest@fbw.vu.nl
Casius
L J R Richard
LJ
D000465:Algorithms; D001642:Bicycling / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity