In the realm of artificial intelligence (AI), genetic algorithm (GA) is a powerful problem-solving technique rooted in the principles of natural evolution. This chapter equips learners with the understanding and tools to use GA in addressing complex challenges. The chapter opens by highlighting the types of problems for which this evolutionary approach is particularly adept. Venturing into the anatomy of GA, the chapter presents the detailed flow of GA that begins with initial population creation and goes through evaluation, selection, crossover, and mutation. It delves into the mechanics and details of each step, offering insights into how GA mimics the process of natural evolution to converge toward optimal solutions. Detailed examples illustrate step by step how GA is implemented to solve a complex problem. The chapter concludes with an engaging exercise that invites learners to apply their newfound knowledge to solve a problem. This hands-on opportunity will empower learners to transcend theory and experiment with application. By mastering GA, learners will gain the ability to navigate the evolutionary landscape of AI.

Authors and Affiliations

1. Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Ishikawa, Japan

   Wei Weng

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Correspondence toWei Weng.

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