Advances in Learning Automata and Intelligent Optimization

This insightful book explores learning automata and their applications in intelligent optimization, providing valuable insights for researchers and practitioners in the field.

The book Advances in Learning Automata and Intelligent Optimization delves into the forefront of research surrounding learning automata (LA) and heuristics, focusing on their applications for both benchmark and real-world optimization challenges. As the use of LA continues to grow as a vital reinforcement learning technique within the realm of artificial intelligence, it becomes essential to equip scholars, scientists, and engineers with a practical understanding of how LA can be leveraged for optimization purposes. The text begins with a concise introduction to LA models tailored for optimization tasks, setting the stage for deeper exploration.

Following the introduction, the book employs bibliometric network analysis to explore various research domains related to LA and optimization. It then examines the role of LA in behavior control within evolutionary computation, alongside discussing memetic models of object migration automata and cellular learning automata that tackle NP-hard problems. This comprehensive approach offers readers insights into the multifaceted applications of LA in diverse contexts.

The latter sections provide an overview of multi-population methods for dynamic optimization problems (DOPs), detailing LA's role in these scenarios and addressing function evaluation management in evolutionary multi-population settings. Advances in Learning Automata and Intelligent Optimization not only presents the latest advancements in LA-based optimization strategies but also emphasizes their practical implications in solving complex optimization issues.