This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.

本书讨论和传播了微分进化（DE）的最先进的研究和发展及其最新进展，如自适应、自适应和混合技术的发展。微分进化是一种基于种群的元启发式技术，用于全球优化，能够处理非微分、非线性和多模式的目标函数。最近在微分进化方面取得了许多进展，从理论到应用都是如此。本书的内容包括微分进化的理论发展、微分进化的性能比较、混合微分进化方法、用于多目标优化的并行和分布式微分进化、软件实现以及现实世界的应用。本书对优化、启发式方法、运筹学和自然计算等学科的研究人员、从业人员和学生都很有用。