Application of an evolutionary algorithm for neural network learning

Tema ovog diplomskog rada bila je Primjena evolucijskog algoritma za učenje neuronske mreže. Opisane su osnove neuronskih mreža i njihove karakteristike kao što su čvorovi i slojevi, te vrste neuronskih mreža. Također opisani su evolucijski algoritmi, inspiracija, struktura algoritma i potrebni koraci. Dan je opis i pregled područja NeuroEvolucije s naglaskom na Neuronske mreže evoluirajućih topologija i težina (eng. Topology and Weight Evolving Artificial Neural Networks - TWEANN) te su postavljena neka vrlo bitna pitanja koje će NEAT algoritam pokušati riješiti. Proučen je teorijski koncept NeuroEvolucije promjenjivih topologija (eng. NeuroEvolution of Augmenting Topologies - NEAT). NEAT algoritam je implementiran u programskom okruženju MATLAB te je implementacija ispitana na dva problema: XOR problem i problem stabilizacije inverznog njihala.The topic of this thesis was Application of an evolutionary algorithm for neural network learning. The basics of neural networks and their characteristics such as nodes and layers, and types of neural networks are described. Evolutionary algorithms, inspiration, algorithm structure and necessary steps are also described. A description and overview of the field of NeuroEvolution with an emphasis on Topology and Weight Evolving Artificial Neural Networks (TWEANN) was given, and some very important questions were asked that the NEAT algorithm will try to solve. The theoretical concept of NeuroEvolution of Augmenting Topologies (NEAT) was studied. The NEAT algorithm was implemented in the MATLAB programming environment, and the implementation was tested on two problems: the XOR problem and the inverse pendulum stabilization problem