Supervised learning with hybrid global optimisation methods

In this chapter, we investigate the basic properties of GLPτS and compare its performance with several other algorithms. In Georgieva & Jordanov (2008a) the method was tested on multimodal mathematical functions of high dimensionality (up to 150), and results were compared with findings of other authors. Here, a summary of these results is presented and subsequently, the method is be employed for NN training of benchmark pattern recognition problems. In addition, few of the more interesting benchmark problems are discussed here. Finally, a case study of machine learning in practice is presented: the NNs trained with GLPτS are employed to recognize and classify seven different types of cork tiles. This is a challenging real-world problem, incorporating computer vision for the automation of production assembly lines (Georgieva & Jordanov, 2008b). Reported results are discussed and compared with similar approaches, demonstrating the advantages of the investigated method.