Evolutionary Psychology: The New Science of the Mind (7版)

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【簡介】 Where did we come from?What is our connection with other life forms?What are the mechanisms of mind that define what it means to be a human being?In the seventh edition of this revolutionary textbook, David M. Buss examines human behavior from an evolutionary perspective, providing students with the conceptual tools needed to study evolutionary psychology and apply them to empirical research on the human mind. Content is organised by topic, beginning with the challenges of survival, mating, parenting, and kinship; progressing to challenges of group living, including cooperation, aggression, sexual conflict and status, prestige, and social hierarchies.Key features of this edition include: Updated and enhanced material based on an explosion of new theories and research, including dozens of new referencesExpanded coverage of topics including socioecology, behavior, emotions, and genderExploration of evolutionary mismatches in several domains such as survival, kinship, and mating, including a discussion of internet datingWith a wealth of student-friendly pedagogy including critical thinking questions and case study boxes supporting the application of evolutionary psychology to real-world situations, this is an invaluable resource for undergraduates studying psychology, biology, and anthropology. The textbook is also supported by a range of instructor resources, including PowerPoint slides, a test bank, and an instructor’s manual, to help students achieve their higher learning goals.

Evolutionary Algorithms (Volume 9)

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【簡介】 Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning. 【目錄】