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Information Science
New "divergent" machine learning algorithms inspired by the evolution of organisms are explained! While many machine learning algorithms including deep learning are "convergent" algorithms that seek one optimization, "divergent" algorithms developed in the research field of artificial life that seek a variety of solutions that cannot be obtained with conventional search methods have attracted attention in recent years. This book focuses on these divergent algorithms, explaining the concept of collective search as the basis of evolutionary computation, the differences from existing convergent algorithms, and their usefulness. New algorithms such as Novelty Search, Quality Diversity, MC, and POET, which have attracted attention recently, are also introduced.