This thesis presents a sophisticated exploration into the use of genetic algorithms (GAs) for the design and optimization of golf courses, integrating both theoretical and practical perspectives. It outlines the challenges of traditional golf course design, including the strategic placement of features such as tees, greens, and hazards, and the need for efficient use of the landscape. By leveraging GAs, the research demonstrates a methodology to optimize these elements by simulating numerous design iterations, which helps in achieving an ideal balance between aesthetic appeal and playability. The core contribution of this
thesis lies in its detailed application of GAs to model and solve the complex problem of golf course layout with considerations for various design constraints like safety, play strategy, and land use efficiency. The results show that GAs can effectively generate diverse, innovative golf course designs that meet specified criteria and constraints, outperforming traditional methods in terms of the quality of the solutions, although at the cost of speed. This study not only enhances the toolkit available to golf course architects but also serves as a prototype for future research in the field. It suggests avenues for further exploration, including the integration of other computational techniques like reinforcement learning and advanced simulation tools, to further refine the design process and tailor golf course layouts to specific environmental and geographical conditions.