中文簡介

IJBIC的主要目標(biāo)是發(fā)表關(guān)于生物激勵計算方法及其應(yīng)用的新研究成果。IJBIC為科學(xué)界和工業(yè)界提供了一種工具，通過該工具可以討論使用兩種或更多傳統(tǒng)和基于計算智能的技術(shù)的想法。生物激勵計算是基于生物系統(tǒng)原理或模型的不同計算方法的總稱。這類方法，如進(jìn)化算法、蟻群優(yōu)化和群體智能，補(bǔ)充了傳統(tǒng)技術(shù)的意義，即前者可以應(yīng)用于對潛在問題知之甚少以及后者遇到困難的大規(guī)模應(yīng)用。因此，面對當(dāng)今要求苛刻的應(yīng)用程序的復(fù)雜性，生物激發(fā)方法變得越來越重要，因此，它們已成功地應(yīng)用于從計算機(jī)工程和機(jī)械工程到化學(xué)工程和分子生物學(xué)的各個領(lǐng)域。IJBIC特別是為了進(jìn)一步全面了解當(dāng)前焦點(diǎn)之外各種生物現(xiàn)象模擬的新算法，即遺傳算法、禁忌搜索等，其目標(biāo)是提高生物計算領(lǐng)域的理論和應(yīng)用。因此，應(yīng)仔細(xì)設(shè)計和適當(dāng)分析算法，并鼓勵作者盡可能評估其結(jié)果的統(tǒng)計有效性。涵蓋的主題包括新的生物啟發(fā)方法來自生活在大自然中的生物人工社會物理/化學(xué)現(xiàn)象新的生物啟發(fā)方法分析工具，例如粗糙集、隨機(jī)過程大腦激發(fā)的方法：模型和算法大數(shù)據(jù)生物激勵計算：算法和結(jié)構(gòu)與生物啟發(fā)方法相關(guān)的應(yīng)用，如生物信息學(xué)

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英文簡介

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