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 includeNew bio-inspired methodologies coming fromcreatures living in natureartificial societyphysical/chemical phenomenaNew bio-inspired methodology analysis tools, e.g. rough sets, stochastic processesBrain-inspired methods: models and algorithmsBio-inspired computation with big data: algorithms and structuresApplications 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
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