A new representation in evolutionary algorithms for the optimization of bioprocesses

Abstract

Evolutionary Algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed-batch fermentation processes. Typically, the optimization purpose is to set the optimal feeding trajectory, being the feeding profile over time given by a piecewise linear function, in order to reduce the number of parameters to the optimization algorithm. In this work, a novel representation scheme for the encoding of the feeding trajectory over time is proposed. Each gene in the variable sized chromosome has two components: a time label and the real value of the variable. The new approach is compared with a traditional real-valued EA, with chromosomes of constant size and fixed discretization steps. Three distinct case studies are presented, taken from previous work from the authors and literature, all considering the optimization of fed-batch fermentation processes. The experimental results show that the proposed approach is capable of results better or at the same level of quality of the best traditional EAs and is able to automatically evolve the best discretization steps for each case, thus simplifying the EA's setup.