进化计算(Evolutionary Computation)是该领域的前沿期刊。它为促进和加强信息交换的研究人员参与理论和实践两方面计算系统提供了一个国际论坛,特别强调进化计算模型如遗传算法、进化策略、分类系统、进化编程和遗传编程。它欢迎来自相关领域的文章,如群体智能(如蚁群优化和粒子群优化),以及其他受自然启发的计算范例(如人工免疫系统)。除了发表描述理论或实验工作的文章外,还欢迎以应用为重点的论文,这些论文描述了在一个应用领域取得的突破性成果,或在现实世界问题的特殊性导致重大算法改进的方法学论文,这些改进可能推广到其他领域。 官网地址:http://dblp.uni-trier.de/db/journals/ec/

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The theory of evolutionary computation for discrete search spaces has made significant of progress in the last ten years. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.

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The theory of evolutionary computation for discrete search spaces has made significant of progress in the last ten years. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.

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