To tackle complex real world problems, scientists have been looking into natural processes and creatures - both as model and metaphor - for years. Optimization is at the heart of many natural processes including Darwinian evolution, social group behavior and foraging strategies. Over the last few decades, there has been remarkable growth in the field of nature-inspired search and optimization algorithms. Currently these techniques are applied to a variety of problems, ranging from scientific research to industry and commerce. The two main families of algorithms that primarily constitute this field today are the evolutionary computing methods and the swarm intelligence algorithms. Although both families of algorithms are generally dedicated towards solving search and optimization problems, they are certainly not equivalent, and each has its own distinguishing features. Reinforcing each other's performance makes powerful hybrid algorithms capable of solving many intractable search and optimization problems.About the journal:Swarm and Evolutionary Computation is the first peer-reviewed publication of its kind that aims at reporting the most recent research and developments in the area of nature-inspired intelligent computation based on the principles of swarm and evolutionary algorithms. It publishes advanced, innovative and interdisciplinary research involving the theoretical, experimental and practical aspects of the two paradigms and their hybridizations. Swarm and Evolutionary Computation is committed to timely publication of very high-quality, peer-reviewed, original articles that advance the state-of-the art of all aspects of evolutionary computation and swarm intelligence. Survey papers reviewing the state-of-the-art of timely topics will also be welcomed as well as novel and interesting applications.Topics of Interest:Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.Applications:Furthermore, the journal fosters industrial uptake by publishing interesting and novel applications in fields and industries dealing with challenging search and optimization problems from domains such as (but not limited to): Aerospace, Systems and Control, Robotics, Power Systems, Communication Engineering, Operations Research and Decision Sciences, Financial Services and Engineering, (Management) Information Systems, Business Intelligence, internet computing, Sensors, Image Processing, Computational Chemistry, Manufacturing, Structural and Mechanical Designs, Bioinformatics, Computational Biology, Mathematical and Computational Psychology, Cognitive Neuroscience, Brain-computer Interfacing, Future Computing Devices, Nonlinear statistical and Applied Physics, and Environmental Modeling and Software.

為了解決復雜的現實世界問題，科學家們多年來一直在研究自然過程和生物——無論是模型還是比喻。優化是許多自然過程的核心，包括達爾文進化論、社會群體行為和覓食策略。在過去的幾十年中，自然啟發的搜索和優化算法領域有了顯著的發展。目前，這些技術被應用于各種問題，從科學研究到工商業。目前主要構成這一領域的兩大算法家族是進化計算方法和群智能算法。雖然這兩個算法家族通常都致力于解決搜索和優化問題，但它們肯定不是等價的，而且每個算法都有其獨特的特點。相互增強的性能使得強大的混合算法能夠解決許多難以解決的搜索和優化問題。關于期刊《群計算與進化計算》是同類刊物中第一本經過同行評議的刊物，旨在報道基于群和進化算法原理的自然啟發智能計算領域的最新研究和發展。它出版先進的、創新的和跨學科的研究，涉及理論、實驗和實踐方面的兩種范式及其雜交。群和進化計算致力于及時出版非常高質量的，同行評議的，原創的文章，推進所有方面的進化計算和群體智能的藝術狀態。此外，我們亦歡迎市民就最新的研究課題發表意見，并提供新穎和有趣的應用。感興趣的題目：感興趣的課題包括但不限于：遺傳算法、遺傳規劃、進化策略、進化規劃、差異進化、人工免疫系統、粒子群、蟻群、細菌覓食、人工蜜蜂、螢火蟲算法、和諧搜索、人工生命、數字生物、分布算法估計、隨機擴散搜索、量子計算、納米計算、膜計算、以人為中心的計算、算法雜交、模因計算、自主計算、自組織系統、組合、離散、二進制、約束、多目標、多模態、動態、大規模優化。應用程序：此外，該期刊還通過在各領域和行業發表有趣和新穎的應用來促進工業的吸收，這些領域和行業處理具有挑戰性的搜索和優化問題（但不限于）：航空航天、系統和控制、機器人、電力系統、通信工程、業務研究和決策科學、金融服務和工程、(管理)信息系統、商業情報、互聯網計算、傳感器、圖像處理、計算化學、制造、結構和機械設計、生物信息學、計算生物學、數學和計算心理學；認知神經科學，腦機接口，未來計算設備，非線性統計和應用物理，環境建模和軟件。