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

2016 Impact Factor Available summer 2017
2014 / 2015 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

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    • 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

Publications in this journal

  • No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: A hybrid network of evolutionary processors (HNEP) is a graph where each node is associated with a special rewriting system called an evolutionary processor, an input filter, and an output filter. Each evolutionary processor is given a finite set of one type of point mutations (insertion, deletion or a substitution of a symbol) which can be applied to certain positions in a string. An HNEP rewrites the strings in the nodes and then re-distributes them according to a filter-based communication protocol; the filters are defined by certain variants of random-context conditions. HNEPs can be considered both as languages generating devices (GHNEPs) and language accepting devices (AHNEPs); most previous approaches treated the accepting and generating cases separately. For both cases, in this paper we show that five nodes are sufficient to accept (AHNEPs) or generate (GHNEPs) any recursively enumerable language by showing the more general result that any partial recursive relation can be computed by an HNEP with (at most) five nodes with the underlying graph structure for the communication between the evolutionary processors being the complete or the linear graph with five nodes, whereas with a star-like communication graph we need six nodes. If the final results are defined by only taking the terminal strings out of the designated output node, then for these extended HNEPs we can prove that only four nodes are needed in all cases—for computing any partial recursive relation as well as for generating and accepting any recursively enumerable language—and the underlying communication structure can be a complete or a linear graph, but now even a star-like graph, too.
    No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: This paper investigates properties of the binary string and language operation overlap assembly which was defined by Csuhaj-Varjú, Petre and Vaszil as a formal model of the linear self-assembly of DNA strands: The overlap assembly of two strings, xy and yz, which share an “overlap” y, results in the string xyz. The study of overlap assembly as a formal language operation is part of ongoing efforts to provide a formal framework and rigorous treatment of DNA-based information and DNA-based computation. Other studies along these lines include theoretical explorations of splicing systems, insertion/deletion systems, substitution, hairpin extension, hairpin reduction, superposition, overlapping catenation, conditional concatenation, contextual intra- and intermolecular recombinations, template guided recombination, as well as directed extension by PCR. In this context, we investigate overlap assembly and its properties: closure properties of basic language families under this operation, decision problems, as well as the possible use of iterated overlap assembly to generate combinatorial DNA libraries.
    No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: Ant colony systems (ACS) have been successfully applied to solving optimization problems. Especially, they are efficient and effective in finding nearly optimal solutions to discrete search spaces. When the solution spaces of the problems to be solved are continuous, it is not so appropriate to use the original ACS to solve it. This paper thus proposes a dynamic-edge ACS algorithm for solving continuous variables problems. It can dynamically generate edges between two nodes and give a pheromone measures for them in a continuous solution space through distribution functions. In addition, it maps the encoding representation and the operators of the original ACS into continuous spaces easily. The proposed algorithm thus provides a simple and standard approach for applying ACS to a problem that has a continuous solution space, and retains the original process and characteristics of the traditional ACS. Several constrained functions are also evaluated to demonstrate the performance of the proposed algorithm.
    No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.
    No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: In this paper, we investigate a single machine problem with actual time-dependent learning effect considering unequal release times, where the objective is to minimize the total completion time. At first, a mathematical model of the problem was formulated, which was verified to be effective by ILOG CP (a constraint programming tool provided by ILOG). Then a branch-and-bound algorithm incorporating with two dominance properties and two lower bounds was developed to obtain solutions for small size problems. However, since this problem is NP-hard, two tabu search algorithms combined with dominance rules, called TSDR, were proposed for solving problems with large number of jobs. The experimental results demonstrated that the proposed branch-and-bound algorithm had a better performance than CP in small size problems. The TSDR algorithms can also obtain optimal solutions for some situations in small problems. In addition, the proposed TSDR algorithms outperformed the benchmark algorithms in the literature and the advantage became more obvious with the number of jobs increasing.
    No preview · Article · Jan 2016 · Natural Computing
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    ABSTRACT: This paper proposes hardware implementation of evolutionary algorithms using dynamic reconfiguration technology. In this paper two types of dynamic reconfiguration for evolutionary algorithm are introduced. The processor was designed by using VHDL and the circuit was simulated. The effectiveness of the proposal processor was confirmed.
    No preview · Article · Dec 2015 · Natural Computing
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    ABSTRACT: An approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity disorders in patients. Obtained results indicate that it is possible to interpret some selected patient's body movements with a sufficiently high effectiveness.
    Preview · Article · Nov 2015 · Natural Computing
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    ABSTRACT: We propose the thesis that randomness is unpredictability with respect to an intended theory and measurement. From this point of view we briefly discuss various forms of randomness that physics, mathematics and computing science have proposed. Computing science allows to discuss unpredictability in an abstract, yet very expressive way, which yields useful hierarchies of randomness and may help to relate its various forms in natural sciences. Finally we discuss biological randomness—its peculiar nature and role in ontogenesis and in evolutionary dynamics (phylogenesis). Randomness in biology has a positive character as it contributes to the organisms’ and populations’ structural stability by adaptation and diversity.
    No preview · Article · Nov 2015 · Natural Computing
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    ABSTRACT: One way to depict a crystallographic structure is by a periodic (di)graph, i.e., a graph whose group of automorphisms has a translational subgroup of finite index acting freely on the structure. We establish a relationship between periodic graphs representing crystallographic structures and an infinite hierarchy of intersection languages (Formula presented.), within the intersection classes of deterministic context-free languages. We introduce a class of counter machines that accept these languages, where the machines with d counters recognize the class (Formula presented.). An intersection of d languages in (Formula presented.) defines (Formula presented.). We prove that there is a one-to-one correspondence between sets of walks starting and ending in the same unit of a d-dimensional periodic (di)graph and the class of languages in (Formula presented.). The proof uses the following result: given a digraph (Formula presented.) and a group G, there is a unique digraph (Formula presented.) such that (Formula presented.) acts freely on the structure, and (Formula presented.).
    No preview · Article · Oct 2015 · Natural Computing
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    ABSTRACT: Physarum Polycephalum is a unicellular and multi-headed slime mold, which can form high efficient networks connecting spatially separated food sources in the process of foraging. Such adaptive networks exhibit a unique characteristic in which network length and fault tolerance are appropriately balanced. Based on the biological observations, the foraging process of Physarum demonstrates two self-organized behaviors, i.e., search and contraction. In this paper, these two behaviors are captured in a multi-agent system. Two types of agents and three transition rules are designed to imitate the search and the contraction behaviors of Physarum based on the necessary and the sufficient conditions of a self-organized computational system. Some simulations of foraging process are used to investigate the characteristics of our system. Experimental results show that our system can autonomously search for food sources and then converge to a stable solution, which replicates the foraging process of Physarum. Specially, a case study of maze problem is used to estimate the path-finding ability of the foraging behaviors of Physarum. What’s more, the model inspired by the foraging behaviors of Physarum is proposed to optimize meta-heuristic algorithms for solving optimization problems. Through comparing the optimized algorithms and the corresponding traditional algorithms, we have found that the optimization strategies have a higher computational performance than their corresponding traditional algorithms, which further justifies that the foraging behaviors of Physarum have a higher computational ability.
    No preview · Article · Oct 2015 · Natural Computing
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    ABSTRACT: In our previous work, a multi-objective evolutionary algorithm (MEA_CDPs) was proposed for detecting separated and overlapping communities simultaneously. However, MEA_CDPs has a couple of defects, like individuals cannot be transformed to community structure by the decoder when the quality of community structure is lower certain thresholds, many vertices with weak overlapping nature are identified as overlapping nodes, and the objective functions can not control the ratio of separated nodes to overlapping nodes. Therefore, in this paper, to overcome these defects, we improve MEA_CDPs by designing more efficient objective functions. We also extend MEA_CDPs’ capability in detecting hierarchical community structures. The improved algorithm is named as iMEA_CDPs. In the experiments, a set of computer-generated networks are first used to test the effect of parameters in iMEA_CDPs, and then four real-world networks are used to validate the performance of iMEA_CDPs. The experimental results show that iMEA_CDPs outperforms MEA_CDPs. Moreover, compared with MEA_CDPs, iMEA_CDPs can detect various kinds of overlapping and hierarchical community structures.
    No preview · Article · Oct 2015 · Natural Computing