Optimal Control Using Evolutionary Algorithms
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Optimal Control Problems involve dynamic systems that are subject to algebraic or differential constraints and whose evolution may be characterized by a performance index. Such a problem can be solved by the well known Evolutionary Algorithms. This paper proposes an evolutionary algorithm having usual characteristics concerning the mutation and crossover operators. Generally speaking, the EA gave good results and the convergence was acceptable. But for a specific problem instance, the evolutionary algorithm underperformed on the first simulation series. Therefore, the paper proposes a new mutation operator having adaptive Gaussian standard deviation of genes' values variation.