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The merits of a parallel genetic algorithm in solving hard optimization problems
A J Knoek van Soest1, L J R Richard Casius
1Faculty 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
Abstract:
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.