Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization
نویسندگان
چکیده
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal highest peaks in space can be more than one. Quality-Diversity are recent addition to evolutionary computation toolbox do not only set of local optima, but instead try illuminate space. In effect, they provide holistic view how high-performing solutions distributed throughout The main differences with multimodal (1) typically works behavioral feature space), and genotypic parameter) space, (2) attempts fill whole behavior even if niche is peak fitness landscape. this chapter, we gentle introduction optimization, discuss representative algorithms, current topics under consideration community. Throughout also several successful applications including deep learning, robotics, reinforcement learning.
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ژورنال
عنوان ژورنال: Springer optimization and its applications
سال: 2021
ISSN: ['1931-6828', '1931-6836']
DOI: https://doi.org/10.1007/978-3-030-66515-9_4