Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-Based Evolutionary Algorithms delivers a comprehensive study of the scheduling challenges faced by electromagnetic detection satellites. Centered on both theoretical models and practical algorithmic solutions, this book is essential for understanding cooperative satellite operations. It is organized into five distinct sections covering background information, foundational models, and advanced evolutionary algorithm frameworks. Readers will explore scheduling strategies for both homogeneous and heterogeneous satellite systems, addressing the detection needs for stationary, low-speed, and high-speed moving targets. The book offers a thorough introduction to current developments in this rapidly evolving field. In addition, the book emphasizes the unique challenges presented by different satellite configurations and target dynamics. It explores the use of reinforcement learning to improve evolutionary scheduling algorithms, showcasing how cooperative planning adapts to varying detection scenarios.