Elucidation of chemical reaction networks through genetic algorithm

PhD ThesisObtaining chemical reaction network experimentally is a time consuming and expensive method. It requires a lot of specialised equipment and expertise in order to achieve concrete results. Using data mining method on available quantitative information such as concentration data of chemical species can help build the chemical reaction network faster, cheaper and with less expertise. The aim of this work is to design an automated system to determine the chemical reaction network (CRN) from the concentration data of participating chemical species in an isothermal chemical batch reactor. Evolutionary algorithm ability to evolve optimum results for a non-linear problem is chosen as the method to go forward. Genetic algorithm’s simplicity is modified such that it can be used to model the CRN with just integers. The developed automated system has shown it can elucidate the CRN of two fictitious CRNs requiring only a few a priori information such as initial chemical species concentration and molecular weight of chemical species. Robustness of the automated system is tested multiple times with different level of noise in system and introduction of unmeasured chemical species and uninvolved chemical species. The automated system is also tested against an experimental data from the reaction of trimethyl orthoacetate and allyl alcohol which had shown mixed results. This had prompted for the inclusion of NSGA-II algorithm in the automated system to increase its ability to discover multiple reactions. At the end of the work, a final form of the automated system is presented which can process datasets from different initial conditions and different operating temperature which shows a good performance in elucidating the CRNs. It is concluded that automated system is susceptible to ‘overfitting’ where it designs its CRN structure to fit the measured chemical species but with enough variation in the data, it had shown it is capable of elucidating the true CRN even in the presence of unmeasured chemical species, noise and unrelated chemical species

Problem Decomposition and Adaptation in Cooperative Neuro-Evolution

One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into subsets, called subcomponents. Cooperative coevolution gains advantage over other methods by evolving particular subcomponents independently from the rest of the network. Its success depends strongly on how the problem decomposition is carried out. This thesis suggests new forms of problem decomposition, based on a novel and intuitive choice of modularity, and examines in detail at what stage and to what extent the different decomposition methods should be used. The new methods are evaluated by training feedforward networks to solve pattern classification tasks, and by training recurrent networks to solve grammatical inference problems. Efficient problem decomposition methods group interacting variables into the same subcomponents. We examine the methods from the literature and provide an analysis of the nature of the neural network optimization problem in terms of interacting variables. We then present a novel problem decomposition method that groups interacting variables and that can be generalized to neural networks with more than a single hidden layer. We then incorporate local search into cooperative neuro-evolution. We present a memetic cooperative coevolution method that takes into account the cost of employing local search across several sub-populations. The optimisation process changes during evolution in terms of diversity and interacting variables. To address this, we examine the adaptation of the problem decomposition method during the evolutionary process. The results in this thesis show that the proposed methods improve performance in terms of optimization time, scalability and robustness. As a further test, we apply the problem decomposition and adaptive cooperative coevolution methods for training recurrent neural networks on chaotic time series problems. The proposed methods show better performance in terms of accuracy and robustness