Yang Yu

Yang Yu behandelt in diesem Buch einen Motivkomplex in dem bisherigen Œuvre von Günter Grass, der seit den siebziger Jahren eine immer bedeutendere Rolle spielt. Grass’ jahrzehntelange Auseinandersetzung mit den Problemen der Dritten Welt wirkt sich nämlich nicht nur entscheidend auf die Entwicklung seines politischen, son-dern auch seines schriftstellerischen bzw. künstlerischen Selbstverständnisses aus. Sie intensiviert und erweitert seine Reflexion über die Dialektik der Aufklärung und forciert seine erneute Hinwendung zum Jugenderlebnis Camus’, was eine Zäsur in seinem literarischen Schaffen darstellt. Ferner zeichnet sich in seinem Umgang mit der Dritten Welt deutlich seine ambivalente Haltung gegenüber den außereuropäischen Kulturen ab. Die Autorin versucht deshalb, aus verschiedenen Perspektiven diesen Motivkreis in seinen Werken zu beleuchten und ein ausdifferenziertes Dritte-Welt-Bild bei ihm herauszuarbeiten, wobei die werkimmanenten Zusammenhänge aufgezeigt werden.

"Wie Fremdes vertraut wird und fremd bleibt" - Die Rolle der Dritten Welt im politischen und literarischen Werk von Günter Grass

Zhi-Hua Zhou

Chao Qian

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

Evolutionary Learning: Advances in Theories and Algorithms

Yang Gao

Edith Elkind

Matthew E. Taylor

This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. Due to the Corona pandemic this event was held virtually.

Yang Yu

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