Natural Computing (Nat Comput)

Publisher: Springer Verlag

Journal description

Natural Computing is a general term referring to computing going on in nature and computing inspired by nature. When complex phenomena going on in nature are viewed as computational processes, our understanding of these phenomena and of the essence of computation is enhanced. In this way one gains valuable insights into both natural sciences and computer science. Characteristic for man-designed computing inspired by nature is the metaphorical use of concepts, principles and mechanisms underlying natural systems. This type of computing includes evolutionary algorithms, neural networks, molecular computing and quantum computing. The aim of the journal is (1) to provide a publication forum for, and to foster links and mutual understanding between researchers from various areas of natural computing, (2) to give researchers in natural sciences and computer science an insight into research trends in natural computing. The research in natural computing is concerned with theory, experiments, and applications, and the journal reports on each of these research lines. Moreover, the journal will cover natural computing in a very broad scope. Thus, e.g., the subarea of evolutionary algorithms will cover also very active research on the boundary of biology and computer science which views the evolution as a computational process, and the subarea of neural networks will also cover computational trends in brain research. The journal is soliciting papers on all aspects of natural computing. Because of the interdisciplinary character of the journal a special effort will be made to solicit survey, review, and tutorial papers which would make research trends in a given subarea more accessible to the broad audience of the journal.

Current impact factor: 0.76

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 0.757
2013 Impact Factor 0.539
2012 Impact Factor 0.683

Impact factor over time

Impact factor

Additional details

5-year impact 0.00
Cited half-life 5.90
Immediacy index 0.05
Eigenfactor 0.00
Article influence 0.00
Website Natural Computing website
Other titles Natural computing (Online)
ISSN 1567-7818
OCLC 50721116
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • Jonathan Hansen · Yaakov Benenson
    Natural Computing 10/2015; DOI:10.1007/s11047-015-9526-1
  • Natural Computing 09/2015; DOI:10.1007/s11047-015-9522-5
  • Natural Computing 09/2015; DOI:10.1007/s11047-015-9523-4
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    ABSTRACT: Multi-valued logical models can be used to describe biological networks on a high level of abstraction based on the network structure and logical parameters capturing regulatory effects. Interestingly, the dynamics of two distinct models need not necessarily be different, which might hint at either only non-functional characteristics distinguishing the models or at different possible implementations for the same behaviour. Here, we study the conditions allowing for such effects by analysing classes of dynamically equivalent models and both structurally maximal and minimal representatives of such classes. Finally, we present an efficient algorithm that constructs a minimal representative of the respective class of a given multi-valued model.
    Natural Computing 09/2015; DOI:10.1007/s11047-015-9525-2
  • Natural Computing 08/2015; DOI:10.1007/s11047-015-9510-9
  • Natural Computing 07/2015; DOI:10.1007/s11047-015-9509-2
  • Natural Computing 07/2015; DOI:10.1007/s11047-015-9508-3
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    ABSTRACT: Image restoration is a research field that attempts to recover a blurred and noisy image. Although we have one-step algorithms that are often fast for image restoration, iterative formulations allow a better control of the trade-off between the enhancement of high frequencies (image details) and noise amplification. Projections onto convex sets (POCS) is an iterative—and parametric-based approach that employs a priori knowledge about the blurred image to guide the restoration process, with promising results in different application domains. However, a proper choice of its parameters is a high computational burden task, since they are continuous-valued and there are an infinity of possible values to be checked. In this paper, we propose to optimize POCS parameters by means of harmony search-based techniques, since they provide elegant and simple formulations for optimization problems. The proposed approach has been validated in synthetic and real images, being able to select suitable parameters in a reasonable amount of time.
    Natural Computing 07/2015; DOI:10.1007/s11047-015-9507-4
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    ABSTRACT: This paper deals with several new methodologies and concepts in the area of rough set theoretic granular computing which are then applied in video tracking. A new concept of neighborhood granule formation over images is introduced here. These granules are of arbitrary shapes and sizes unlike other existing granulation techniques and hence more natural. The concept of rough-rule base is used for video tracking to deal with the uncertainties and incompleteness as well as to gain in computation time. A new neighborhood granular rough rule base is formulated which proves to be effective in reducing the indiscernibility of the rule-base. This new rule-base provides more accurate results in the task of tracking. Two indices to evaluate the performance of tracking are defined. These indices do not need ground truth information or any estimation technique like the other existing ones. All these features are demonstrated with suitable experimental results.
    Natural Computing 05/2015; DOI:10.1007/s11047-015-9493-6
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    ABSTRACT: We study the set of output stable configurations of chemical reaction deciders (CRDs). It turns out that CRDs with only bimolecular reactions (which are almost equivalent to population protocols) have a special structure that allows for an algorithm to efficiently compute their finite set of minimal output unstable configurations. As a consequence, a relatively large set of configurations may be efficiently checked for output stability. We also provide a number of observations regarding the semilinearity result of Angluin et al. [Distrib. Comput., 2007] from the context of population protocols (which is a central result for output stable CRDs). In particular, we observe that the computation-friendly class of totally stable CRDs has equal expressive power as the larger class of output stable CRDs.
    Natural Computing 05/2015; DOI:10.1007/s11047-015-9506-5
  • Natural Computing 05/2015; DOI:10.1007/s11047-015-9502-9
  • Natural Computing 05/2015; DOI:10.1007/s11047-015-9498-1
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    ABSTRACT: We present general results that are useful in showing closure and decidable properties of large classes of languages with respect to biologically-inspired operations. We use these results to prove new decidability results and closure properties of some classes of languages under bio-operations such hairpin-inversion, the recently studied operation of pseudo-inversion, and other bio-operations. We also provide techniques for proving undecidability results. In particular, we give a new approach for proving the undecidability of problems for which the usual method of reduction to the undecidability of the Post Correspondence Problem seems hard to apply. Our closure and decidability results strengthen or generalize previous results.
    Natural Computing 05/2015; DOI:10.1007/s11047-015-9500-y
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    ABSTRACT: Encoding feasible solutions is one of the most important aspects to be taken into account in the field of evolutionary computation in order to solve search or optimization problems. This paper proposes a new encoding scheme for real-coded evolutionary algorithms. It is called partition based encoding scheme, and satisfies two restrictions. Firstly, each of the components of a decoded vector that conforms a candidate solution to a problem at hand belongs to a predefined interval. Secondly, the sum of the components of each of these decoded vectors is always equal to a predefined constant. The proposed encoding scheme inherently guarantees these constraints for all the individuals that are generated within the evolution process as a consequence of applying the genetic operators. Partition based encoding scheme is successfully applied to learning conditional probability tables for a given discrete Bayesian network topology, where each row of the tables must exactly add up to one, and the components of each row belong to the interval [0,1] as they are probability values. The results given by the proposed encoding system for this learning problem is compared to a deterministic algorithm and another evolutionary approach. Better results are shown in terms of accuracy with respect to the former one, and accuracy and convergence speed with respect to the later one.
    Natural Computing 05/2015; DOI:10.1007/s11047-015-9505-6
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    ABSTRACT: Nature-inspired algorithms have been applied in the optimization field including digital image processing like image enhancement or segmentation. Firefly algorithm (FA) is one of the most powerful of them. In this paper two different implementation of FA has been taken into consideration. One of them is FA via lévy flight where step length of lévy flight has been taken from chaotic sequence. Chaotic sequence shows ergodicity property which helps in better searching. But in the second implementation chaotic sequence replaces lévy flight to enhance the capability of FA. Population of individuals has been created in every generation using the information of population diversity. As an affect FA does not converges prematurely. These two modified FA algorithms have been applied to optimize parameters of parameterized contrast stretching function. Entropy, contrast and energy of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it has been shown that FA with chaotic sequence and population diversity information outperforms the Particle swarm optimization and FA via lévy flight.
    Natural Computing 03/2015; DOI:10.1007/s11047-015-9496-3
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    ABSTRACT: The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.
    Natural Computing 03/2015; DOI:10.1007/s11047-015-9495-4
  • Source
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    ABSTRACT: Modelling and simulation of complex systems can create scientific research tools that allow the inaccessible dynamic aspects of systems to be explored in ways that are not possible in live systems. In some scientific contexts, there is a need to be able to create and use such simulations to explore and generate hypotheses alongside conventional laboratory research. The principled complex systems modelling and simulation (CoSMoS) approach was created to support these activities, as a response to a perceived gap in the software engineering development process for simulation. The article presents some of the software engineering motivation for CoSMoS, by exploring this perceived gap. Following from this analysis, the article considers the validation of complex systems simulators, especially where these are to be used in ongoing research.
    Natural Computing 03/2015; 14(1). DOI:10.1007/s11047-014-9462-5