Еволюційні архітектури в штучних нейронних мережах

Abstract

This thesis delves into the concept of neuroevolution, which combines the merits of these two systems, highlighting how artificial intelligence can harness natural evolution principles to enhance neural network efficiency. Key facets such as the Ant Colony Optimization, a bio-inspired algorithm modelled on ant foraging behavior, are explored in-depth. Challenges like architectural optimization, algorithm limitations, and scalability are analyzed, highlighting potential solutions like surrogate models. As a conclusion, the research underscores the dynamic potential of evolutionary algorithms in powering artificial neural networks, foreseeing continued advancement in this domain.