As cities expand and aim to become more environmentally sustainable and livable, enhancing the efficiency of public transportation systems becomes increasingly important. Addressing urban congestion and air quality issues necessitates innovative solutions for sustainable transit. This thesis explores the application of Genetic Algorithms (GA) to optimize bus routes, directly addressing the need for more sustainable urban transit solutions and aiming to make public transport a more attractive option for passengers.

The algorithm's development can be divided into two main parts. First, a simpler model was developed and tested on the Mandl Netork, a simple theoretical transportation network. Second, the algorithm was adapted and tested on a real-world network in the municipality of Halmstad. The research explores the complexities of transit network design, focusing on optimizing the networks based on several criteria. This approach aims to enhance the efficiency of public transportation and its appeal to users. 

The results show that the Genetic Algorithm effectively optimized the Mandl Network, achieving the best possible operator score and highlighting its ability to fine-tune less complex networks to their optimal states. This success illustrates the GA's capability to deliver good solutions, improving route efficiency and potentially reducing costs. In the more extensive Halmstad case study, the algorithm demonstrated quick adaptability, achieving relatively good scores quickly. This capability could be crucial for fast adjustments in response to disruptions such as subway cancellations or ongoing road constructions, ensuring the continued efficiency of public transportation. The algorithm's ability to quickly generate effective solutions confirms its suitability for dynamic environments. It offers a valuable tool for urban planners when developing more adaptive and resilient urban transport systems.