Hybrid Evolutionary Clonal Selection for Parameter Estimation of Biological Model

The Clonal Selection Algorithm (CSA) is a widely used Artificial Immune Optimization (AIO) approach that tends to mimic the immune response when the pathogenic pattern is detected by the immune cells. However, this method, in its standard form, shows slow convergence and frequently traps in one of the local optima, especially for high dimensional problems. Hence, in this paper, an improved CSA method is introduced by integrating evolutionary operations adopted from the Differential Evolution (DE) method. The proposed method, called Differential Clonal Evolution (DICE) method, utilizes the mutation and crossover operation to exploit the information of different antibodies in the population. Furthermore, antibodies that yield trivial fitness value are relocated randomly so that the method can escape from the local optima in a more straightforward manner. To show the effectiveness of this method, the method is used to estimate parameters of a bacterial lactose production model using noisy and incomplete time series data. The statistical results suggest that the proposed method has better speed and accuracy performance compared to the standard CSA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques. Keywords— Clonal Selection; Differential Evolution; Hybrid Optimization; Parameter Estimation; Bacterial