## 1 Introduction  
Neuroevolution consists of using evolutionary algorithms in training artificial neural networks. Unlike traditional, gradient-based training methods, neuroevolution can optimize the parameters of a neural network, i.e., weights and biases, and its hyperparameters, i.e., the number of hidden layers, activation functions, learning rate, etc. Neuroevolution is also suitable for both supervised and reinforcement learning applications. In this paper, feed-forward neural networks are trained using biologically inspired optimization algorithms analyzed in previous work [1], and we propose different strategies for combining these algorithms. According to the No Free Lunch (NFL) theorem [2], there is no algorithm that can provide superior performance to all other techniques in solving all optimization problems [3, 4]. Motivated by this theorem, we propose using different algorithms to learn the architecture, weights, and biases of a multilayer perceptron (MLP), in order to generate suitable regression models for the process of the free radical polymerization of methyl methacrylate (MMA). Polymerization processes are generally notorious for the difficulty in finding suitable regression models to properly characterize them and to allow for making reliable predictions. Aside from the complexity of the underlying reactions, the phenomenology behind these processes is often not fully understood. As such, approximations of the actual phenomena have to be made when analyzing them, which adversely affects the accuracy and convergence of most conventional regression methods. Furthermore, the related mathematical models are themselves, of high complexity, which causes difficulties when solving them, requiring considerable computational resources and making them unusable in online control and optimization scenarios. Under such circumstances, empirical modeling is often the preferred approach. In this context, MMA is among the more difficult to model chemical processes, consisting of complex reactions which cause difficulties in building a phenomenological model based on mass and energy balances. In addition to the multitude of elementary reactions and species, a volume contraction also takes place during the process. Moreover, the significant increase in viscosity from a certain moment of the reaction determines the decrease in the diffusion rate of the polymer and monomer molecules. In free radical polymerization, such diffusional aspects (gel and glass effects) must be quantified by relations which should render the variation of the propagation and termination rate constants with the conversion. This is a difficult part to model, especially because not all the aspects of these phenomena have been completely explained. In order to overcome the difficulties related to the modeling of the controlled diffusion phenomena, neural networks prove to be suitable tools for modeling, provided that they are developed in a near-optimal version for both structure and parameters. The optimization goal is to find MLPs with the best prediction performance, i.e., the minimization of prediction error, on the MMA data set (conversion and molecular masses depending on reaction conditions). The optimization algorithms we use are the Football Game Algorithm (FGA) [5], Imperialist Competitive Algorithm (ICA) [6], Simple Human Learning Optimization (SHLO) [7], Social Learning Optimization (SLO) [8], Teaching-Learning-Based Optimization (TLBO) [9], Viral System (VS) [10], and Virulence Optimization Algorithm (VOA) [11]. The main contribution of the paper is finding a neural network optimizer that generates optimal neural network-based regression models for a proper representation of the MMA process. Our studies show that conventional regression models fail to properly achieve this task.