Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Experimental Comparisons of Derivative Free Optimization Algorithms
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
A novel evolutionary drug scheduling model in cancer chemotherapy
IEEE Transactions on Information Technology in Biomedicine
Optimal and sub-optimal quarantine and isolation control in SARS epidemics
Mathematical and Computer Modelling: An International Journal
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In many disease models, the dynamics are described by a system of differential equations. When the spread of the disease is controlled by a treatment strategy, an obvious challenge is to find the best treatment possible. Mathematically, this problem is known as optimal control, or dynamic optimization. To solve these problems, researchers are increasingly turning to evolutionary optimization methods. Evolutionary computation, however, operates on discrete, n-dimensional vectors, not on continuous functions, and becomes computationally unmanageable for large n. Thus a parameterization technique is required, that can represent arbitrary functions with a small number of parameters. The typical approach to parameterization in epidemiological and biomedical models is to approximate the control functions as piecewise constant. We show the limitations of this approach, and demonstrate a recently developed method, Bézier Control Parameterization (BCP). With relatively few parameters, BCP can represent continuous control functions, and provides an efficient and effective parameterization method for evolutionary control of disease models.