Genetic AI: Algorithms, Theory, and Computational Evolution

Genetic AI explores how evolutionary principles shape digital intelligence. Part I traces the roots from Darwin to Turing, explaining how populations, fitness, and genotype–phenotype mappings form the conceptual core. Part II introduces the main algorithmic toolkit: Genetic Algorithms for combinatorial search, Evolution Strategies for continuous optimization, Genetic Programming for evolving code, and other paradigms including Evolutionary Programming, Differential Evolution, and multi-modal or multi-objective optimization. Part III focuses on practical applications such as neuroevolution for evolving neural networks, evolutionary robotics for adaptive embodied agents, developmental systems for generative complexity, interactive evolutionary computation driven by human preference, and quality-diversity methods enabling open-ended exploration. Part IV covers the theoretical foundations and large-scale implementations that support efficient evolutionary computation. Part V looks ahead to the future, addressing emerging capabilities and the ethical implications of self-evolving autonomous systems.