In contrast to the functional expressions or syntax trees used in traditional genetic programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as the primary genetic material characteristics are exploited to achieve acceleration of both execution time and the progress of evolution. This book investigates typical phenomena such as code GP non-effective, neutral variation and growth of the code from the perspective of linear GP. This text is divided into three sections, each describing the details of methodology and applications. Part I introduces the basic concepts of linear GP and presents efficient algorithms to analyze and optimize a linear genetic program at runtime. Part III explore more advanced techniques and phenomena, including the size of effective control measures, control diversity, code growth, and neutral variation. This book provides a solid introduction to the field of linear GP, and the review, a more comprehensive details of the principles and techniques.