The major goal of IJBIC is the publication of new research results on bio-inspired computation methods and their applications. IJBIC provides the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques can be discussed. Bio-inspired computation is an umbrella term for different computational approaches that are based on principles or models of biological systems. This class of methods, such as evolutionary algorithms, ant colony optimisation, and swarm intelligence, complements traditional techniques in the sense that the former can be applied to large-scale applications where little is known about the underlying problem and where the latter approaches encounter difficulties. Therefore, bio-inspired methods are becoming increasingly important in the face of the complexity of today's demanding applications, and accordingly they have been successfully used in various fields ranging from computer engineering and mechanical engineering to chemical engineering and molecular biology. IJBIC is especially intended for furthering the overall understanding of new algorithms simulated with various bio-phenomena beyond the current focus, i.e. genetic algorithms, Tabu search, etc. Its objective is improvement in theory and applications of the bio-computation field. Algorithms should therefore be carefully designed and appropriately analysed, and authors are encouraged to assess the statistical validity of their results whenever possible. Topics covered include New bio-inspired methodologies coming from creatures living in nature artificial society physical/chemical phenomena New bio-inspired methodology analysis tools, e.g. rough sets, stochastic processes Brain-inspired methods: models and algorithms Bio-inspired computation with big data: algorithms and structures Applications associated with bio-inspired methodologies, e.g. bioinformatics
IJBIC的主要目标是发表关于生物激励计算方法及其应用的新研究成果。IJBIC为科学界和工业界提供了一种工具,通过该工具可以讨论使用两种或更多传统和基于计算智能的技术的想法。 生物激励计算是基于生物系统原理或模型的不同计算方法的总称。这类方法,如进化算法、蚁群优化和群体智能,补充了传统技术的意义,即前者可以应用于对潜在问题知之甚少以及后者遇到困难的大规模应用。因此,面对当今要求苛刻的应用程序的复杂性,生物激发方法变得越来越重要,因此,它们已成功地应用于从计算机工程和机械工程到化学工程和分子生物学的各个领域。 IJBIC特别是为了进一步全面了解当前焦点之外各种生物现象模拟的新算法,即遗传算法、禁忌搜索等,其目标是提高生物计算领域的理论和应用。因此,应仔细设计和适当分析算法,并鼓励作者尽可能评估其结果的统计有效性。 涵盖的主题包括 新的生物启发方法来自 生活在大自然中的生物 人工社会 物理/化学现象 新的生物启发方法分析工具,例如粗糙集、随机过程 大脑激发的方法:模型和算法 大数据生物激励计算:算法和结构 与生物启发方法相关的应用,如生物信息学
期刊ISSN
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1758-0366 |
最新的影响因子
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3.5 |
最新CiteScore值
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3.53 |
最新自引率
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18.60% |
期刊官方网址
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http://www.inderscience.com/jhome.php?jcode=ijbic |
期刊投稿网址
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http://www.inderscience.com/ospeers/authorregister.php |
通讯地址
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偏重的研究方向(学科)
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COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER |
出版周期
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平均审稿速度
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>12周,或约稿 |
出版年份
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0 |
出版国家/地区
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ENGLAND |
是否OA
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No |
SCI期刊coverage
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Science Citation Index Expanded(科学引文索引扩展) |
NCBI查询
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PubMed Central (PMC)链接 全文检索(pubmed central) |
最新中科院JCR分区
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大类(学科)
小类(学科)
JCR学科排名
工程技术
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(计算机科学,人工智能) 2区
COMPUTER SCIENCE, THEORY & METHODS(计算机科学,理论和方法) 1区
48/132
22/103
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最新的影响因子
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3.5 | |||||||
最新公布的期刊年发文量 |
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总被引频次 | 968 | |||||||
特征因子 | 0.001080 | |||||||
影响因子趋势图 |
2007年以来影响因子趋势图(整体平稳趋势)
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最新CiteScore值
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3.53
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引文计数(2018)
文献(2015-2017)
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346次引用
98篇文献
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文献总数(2014-2016) | 98 | ||||||||||
被引用比率
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73% | ||||||||||
SJR
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0.721 | ||||||||||
SNIP
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1.87 | ||||||||||
CiteScore排名
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CiteScore趋势图 |
CiteScore趋势图
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scopus涵盖范围 |
scopus趋势图
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本刊同领域相关期刊
|
|
期刊名称 | IF值 |
CHEMPHYSCHEM | 2.9 |
CHEMICAL PHYSICS | 2.3 |
MOLECULAR SIMULATION | 2.1 |
CHEMICAL PHYSICS LETTERS | 2.8 |
JOURNAL OF MOLECULAR LIQUIDS | 6 |
JOURNAL OF PHYSICAL CHEMISTRY A | 2.9 |
RADIATION PHYSICS AND CHEMISTRY | 2.9 |
PHYSICAL CHEMISTRY CHEMICAL PHYSICS | 3.3 |
Journal of Physical Chemistry Letters | 5.7 |
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