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Exploring Open-Ended Algorithms: POET

Machine Learning Street Talk (MLST)

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Evolving Intelligence: Algorithms and Environments

This chapter explores the intricacies of evolutionary algorithms and genetic programming, emphasizing mutation, recombination, and the co-evolution of agents and their environments. It highlights the limitations of the POET algorithm, critiques traditional optimization methods, and discusses the balance between diversity and specialization in agent populations. Through a comparison of various approaches, the speakers unveil the challenges and potentials of creating more versatile AI systems across complex environments.

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