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A further limitation of ANNs is that, due to local minima in the cost function, optimizations starting from different initial parameters, often ends up at different minima.
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Finally, Section Objective function optimization describes the algorithm to optimize the objective function.
Our independent assessment of the parameters with data not used for cost function optimization allowed us to further restrict the minima and reach a compromise between cost function optimality and biological plausibility.
Consequently, they can be analyzed by means of linear programming to find an optimal metabolic flux profile as defined by a linear objective function (optimization criterion).
We consider a general utility function optimization objective.
For function optimization, EP mainly relies on its mutation operators.
Four illustrative examples of Lyapunov function optimization and parameter modification are given.
A subset of these is selected for detailed examination employing multiobjective function optimization techniques.
Professor Stultz's interests include molecular simulations, protein structure and dynamics, biophysics, disease models, atherosclerosis, Alzheimer's disease, rheumatoid arthritis, stochastic models, and function optimization.
Differential evolution (DE) is a simple and efficient evolutionary algorithm for function optimization over continuous spaces.
This algorithm is applied to nonlinear function optimization and data clustering problems for performance evaluation.
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