The merits of a parallel genetic algorithm in solving hard optimization problems.

A J Knoek van Soest; L J R Richard Casius

doi:10.1115/1.1537735

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The merits of a parallel genetic algorithm in solving hard optimization problems.

A J Knoek van Soest·L J R Richard Casius

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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.

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Algorithms Bicycling Computer Simulation Humans Models, Biological Movement Muscle, Skeletal Psychomotor Performance Quality Control Reproducibility of Results Sensitivity and Specificity Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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A J Knoek van Soest, L J R Richard Casius. 2003. The merits of a parallel genetic algorithm in solving hard optimization problems.. https://doi.org/10.1115/1.1537735

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