"Generative AI or LLM (Large Language Model) is currently flourishing, but its core technology is mainly based upon correlation inference from big data, making it difficult to derive deep knowledge. For example, multi-fidelity is an important essence of human decision, but it cannot be realized by means of conventional deep learning. This book provides theoretical and practical knowledge about swarm and evolutionary approach, i.e., deep swarm and deep evolution, to generative AI. While the central theme of the book is generative AI, it also develops a discussion of AI in a broader sense. The development of such tools contributes for better optimizing methodologies with the integration of several machine learning and deep learning techniques. In particular, we will discuss how the "emergence" concept can contribute to the improvement of AI. Another goal of this book is to model human cognitive function in terms of "emergence" and to explain the feasibility of AI. In other words, to understand how intelligence emerges, to map it to the real world, and to provide causal explanations by means of evolutional and psychological mechanisms. To this end, this book focuses on human perceptions of utility and multi-fidelity. We describe the emergence of various cognitive errors, and irrational behaviors in the above-mentioned multi-objective situations. Furthermore, this book illustratively describes the intelligent behavior of living organisms. For instance, we explain how ants choose a preferred nest while solving a kind of optimal problem (i.e., Buffon's needle problem) and how bees choose a new nest while taking a majority vote. This is to clarify how to achieve AI in the direction of artificial life. We also describe sexual selection, which is a well-known natural phenomenon that troubled Darwin, i.e., why evolutionarily useless items evolved such as peacock feathers and moose antlers etc. Sexual selection is extended as "novelty search" for the application of generative AI. Yet another emphasis is its real-world applicability. We provide empirical examples from real-world data to show that deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, time-series prediction, predictive control and image generation etc"--