In many experimental setting, we are concerned with finding the optimal experimental design, i.e. the configuration of predictive variables corresponding to an optimal value of the response. However, the high dimensionality of the search space, the vast number of variables and the economical constrains limit the ability of classical techniques to reach the optimum of a function. In this paper, we investigate the combination of statistical modeling and optimization algorithms to better explore the combinatorial search space and increase the performance of classical approaches. To this end, we propose a Model based Ant Colony Design (MACD) based on statistical modelling and Ant Colony Optimization. We apply the novel technique to a simulative case study related to Synthetic Biology.
Borrotti, M., De Lucrezia, D., Minervini, G., Poli, I. (2010). A Model Based Ant Colony Design for the Protein Engineering Problem. In Swarm Intelligence (pp.352-359). Springer Nature [10.1007/978-3-642-15461-4_31].
A Model Based Ant Colony Design for the Protein Engineering Problem
Borrotti, M;
2010
Abstract
In many experimental setting, we are concerned with finding the optimal experimental design, i.e. the configuration of predictive variables corresponding to an optimal value of the response. However, the high dimensionality of the search space, the vast number of variables and the economical constrains limit the ability of classical techniques to reach the optimum of a function. In this paper, we investigate the combination of statistical modeling and optimization algorithms to better explore the combinatorial search space and increase the performance of classical approaches. To this end, we propose a Model based Ant Colony Design (MACD) based on statistical modelling and Ant Colony Optimization. We apply the novel technique to a simulative case study related to Synthetic Biology.
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