Natural Computing (Nat Comput)
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.
- WebsiteNatural Computing website
Other titlesNatural computing (Online)
Material typeDocument, Periodical, Internet resource
Document typeInternet Resource, Computer File, Journal / Magazine / Newspaper
- Author can archive a pre-print version
- Author can archive a post-print version
- Authors own final version only can be archived
- Publisher's version/PDF cannot be used
- On author's website or institutional repository
- On funders designated website/repository after 12 months at the funders request or as a result of legal obligation
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- Must link to publisher version
- Set phrase to accompany link to published version (The original publication is available at www.springerlink.com)
- Articles in some journals can be made Open Access on payment of additional charge
Publications in this journal
Article: Hierarchical Self Assembly of Patterns from the Robinson Tilings: DNA Tile Design in an Enhanced Tile Assembly Model.[show abstract] [hide abstract]
ABSTRACT: We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution.Natural Computing 06/2012; 11(2):323-338.
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ABSTRACT: The Topological Active Volumes is an active model focused on 3D segmentation tasks. It is based on the 2D Topological Active Nets model and provides information about the surfaces and the inside of the detected objects in the scene. This paper proposes new optimization approaches based on Genetic Algorithms that improve the results of the 3D segmentations and overcome some drawbacks of the model related to parameter tuning or noise conditions. The hybridization of the genetic algorithm with a greedy local search allows the treatment of topological changes in the model, with the possibility of an automatic subdivision of the Topological Active Volume. This combination integrates the advantages of the global and local search procedures in the segmentation process. KeywordsGenetic algorithms–Image segmentation–Hybrid optimization algorithms–Lamarck strategy–Topological Active VolumesNatural Computing 05/2012; 11(1):161-174.
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ABSTRACT: In this paper we present a new software tool for dealing with the problem of segmentation in Digital Imagery. The implementation is inspired in the design of a tissue-like P system which solves the problem in constant time due the intrinsic parallelism of Membrane Computing devices. KeywordsMembrane computing–Digital Imagery–SegmentationNatural Computing 05/2012;
Article: (Tissue) P systems working in the k-restricted minimally or maximally parallel transition mode[show abstract] [hide abstract]
ABSTRACT: We investigate variants of the maximally and the minimally parallel transition mode, i.e., we allow only a bounded number of rules to be taken from every set of the partitioning of the whole set of rules. The 1-restricted minimally parallel transition mode especially fits to describe the way transitions take place in spiking neural P systems without delays, i.e., in every neuron where a rule is applicable exactly one rule has to be applied. Moreover, purely catalytic P systems working in the maximally parallel transition mode can be described as P systems using the corresponding rules without catalysts, i.e., noncooperative rules, when working in the 1-restricted minimally parallel transition mode. In contrast to these results for computationally complete models of P systems, with the k-restricted maximally parallel transition mode noncooperative rules only allow for the generation of semi-linear sets. KeywordsCatalysts–Computational completeness–P systems–Spiking neural P systems–Transition modesNatural Computing 05/2012; 10(2):821-833.
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ABSTRACT: We characterize the classes of languages over finite alphabets which may be described by P automata, i.e., accepting P systems with communication rules only. Motivated by properties of natural computing systems, and the actual behavior of P automata, we study computational complexity classes with a certain restriction on the use of the available workspace in the course of computations and relate these to the language classes described by P automata. We prove that if the rules of the P system are applied sequentially, then the accepted language class is strictly included in the class of languages accepted by one-way Turing machines with a logarithmically bounded workspace, and if the rules are applied in the maximally parallel manner, then the class of context-sensitive languages is obtained.Natural Computing 05/2012; 5(2):109-126.
Article: A relation by palindromic subwords[show abstract] [hide abstract]
ABSTRACT: We define a new relation on words by a finite series of insertions and/or deletions of palindromic subwords. In particular we concentrate on insertion or deletion of Watson–Crick palindromes. We show that the new relation ∼θ is, in fact, an equivalence relation where θ is any arbitrary antimorphic involution that is not the identity on the letters of the alphabet. We also show that the set of all θ-palindromic free words defined in (Daley etal. in preparation) is ∼θ-independent. Using the relation we define a new subclass of primitive words which we call as ∼θ-primitive words and show that the class of all ∼θ-primitive words is closed under circular permutations. We also define ∼θ-conjugates and ∼θ-commutativity and study the properties of such words and show that they are similar to that of conjugate words and words that commute. KeywordsDNA encodings-Combinatorics of words-Palindromes-Insertion/deletion-Watson–Crick palindromesNatural Computing 04/2012; 9(4):935-954.
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ABSTRACT: We provide the description for the nondeterministic waiting time (NWT) algorithm, a biochemical modeling approach based on the membrane systems paradigm of computation. The technique provides a unique (different to Gillespie’s algorithm or ODE modeling) perspective on the biochemical evolution of the cell. That is, depending on the reactions and molecular multiplicities of a given model, our simulator is capable of producing results comparable to the alternative techniques—continuous and deterministic or discrete and stochastic. Some results for sample models are given, illustrating the differences between the NWT algorithm, the Gillespie algorithm, and the solutions to systems of ordinary differential equations. We have previously used this simulation technique to address issues surrounding Fas-induced apoptosis in cancerous cells and so-called latent HIV-infected cells. KeywordsSystems biology–Biochemical modeling–Stochastic–Discrete–Gillespie algorithm–Lotka–Volterra–Circadian rhythmNatural Computing 04/2012; 10(1):139-149.
Article: An immune system inspired clustering and classification method to detect critical areas in electrical power networks[show abstract] [hide abstract]
ABSTRACT: Identifying critical, failure prone areas in a power system network are often a difficult and computationally intensive task. Artificial Immune System (AIS) algorithms have been shown to be capable of generalization and learning to identify previously unseen patterns. In this paper, a method is developed that uses artificial immune system classification and clustering algorithms to identify critical areas in the network. The algorithm identifies areas of the power system network that are prone to voltage collapse and areas with overloaded lines. The applicability of AIS for this particular task is demonstrated on test electrical power system networks. Its accuracy is compared with an optimised support vector machine (SVM) algorithm and k nearest neighbours algorithm (kNN) across 3 different power system networks. KeywordsClustering–Classification–Artificial immune systems–AIS–Power systems–Critical network areas–Overloaded linesNatural Computing 04/2012; 10(1):305-333.
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ABSTRACT: Relying on a convenient logical representation of regulatory networks, we propose a generic method to qualitatively model regulatory interactions in the standard elementary and coloured Petri net frameworks. Logical functions governing the behaviours of the components of logical regulatory graphs are efficiently represented by Multivalued Decision Diagrams, which are also at the basis of the translation of logical models in terms of Petri nets. We further delineate a simple strategy to sort trajectories through the introduction of priority classes (in the logical framework) or priority functions (in the Petri net framework). We also focus on qualitative behaviours such as multistationarity or sustained oscillations, identified as specific structures in state transition graphs (for logical models) or in marking graphs (in Petri nets). Regulatory circuits are known to be at the origin of such properties. In this respect, we present a method that allows to determine the functionality contexts of regulatory circuits, i.e. constraints on external regulator states enabling the corresponding dynamical properties. Finally, this approach is illustrated through an application to the modelling of a regulatory network controlling T lymphocyte activation and differentiation. KeywordsGene regulation–Biological networks–Regulatory circuits–Logical modelling–Petri nets–Signal transduction–Cell differentiationNatural Computing 04/2012; 10(2):727-750.
Article: Grammar-based immune programming[show abstract] [hide abstract]
ABSTRACT: This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyze algorithm behavior over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) =g(x), and to find a symbolic representation of a discontinuous function. keywordsArtificial immune system–Grammatical evolution–Immune programming–Symbolic regression–Model inferenceNatural Computing 04/2012; 10(1):209-241.
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ABSTRACT: Any crossover operator has both beneficial and detrimental effects: it can bring building blocks together or it can tear them apart. In this paper, we provide evidence that the recombination can be biased towards its more beneficial aspects by modifying both the parent selection process and the number of children created by each pair of parents. We exclude both high rank and low rank individuals from being selected as parents. The new idea is that the worst individuals do not have valuable building blocks to contribute, and it is too risky to subject the best individuals to crossover and have their building blocks separated. In a further refinement, we allow the number of children per family to be correlated to the diversity of their parents, and thus increase the pressure of sibling rivalry (competition). These ideas are tested on well-known test functions such as the hierarchical if-and-only-if, royal road, concatenated trap functions and the one dimensional Ising spin model. Four different parent selection schemes are compared and simulations are shown for both two children (fixed) and many children (variable) families. The results indicate that these changes are beneficial for a wide class of problems. KeywordsParent selection-Building-block hypothesis-Crossover operator-Recombination operatorNatural Computing 04/2012; 9(1):263-282.
Article: Bond computing systems: a biologically inspired and high-level dynamics model for pervasive computing[show abstract] [hide abstract]
ABSTRACT: Targeting at modeling the high-level dynamics of pervasive computing systems, we introduce bond computing systems (BCS) consisting of objects, bonds and rules. Objects are typed but addressless representations of physical or logical (computing and communicating) entities. Bonds are typed multisets of objects. In a BCS, a configuration is specified by a multiset of bonds, called a collection. Rules specify how a collection evolves to a new one. A BCS is a variation of a P system introduced by Gheorghe Paun where, roughly, there is no maximal parallelism but with typed and unbounded number of membranes, and hence, our model is also biologically inspired. In this paper, we focus on regular bond computing systems (RBCS), where bond types are regular, and study their computation power and verification problems. Among other results, we show that the computing power of RBCS lies between linearly bounded automata (LBA) and LBC (a form of bounded multicounter machines) and hence, the regular bond-type reachability problem (given an RBCS, whether there is some initial collection that can reach some collection containing a bond of a given regular type) is undecidable. We also study a restricted model (namely, B-boundedness) of RBCS where the reachability problem becomes decidable. KeywordsP system-Bond computing system-ReachabilityNatural Computing 04/2012; 9(2):347-364.
Article: Stochastic automated search methods in cellular automata: the discovery of tens of thousands of glider guns[show abstract] [hide abstract]
ABSTRACT: This paper deals with the spontaneous emergence of glider guns in cellular automata. An evolutionary search for glider guns with different parameters is described and other search techniques are also presented to provide a benchmark. We demonstrate the spontaneous emergence of an important number of novel glider guns discovered by an evolutionary algorithm. An automatic process to identify guns leads to a classification of glider guns that takes into account the number of emitted gliders of a specific type. We also show it is possible to discover guns for many other types of gliders. Significantly, all the found automata can be candidate to an automatic search for collision-based universal cellular automata simulating Turing machines in their space-time dynamics using gliders and glider guns. KeywordsEmergence of complexity-Evolutionary algorithm-Universality-Cellular automata-Glider gun-ClassificationNatural Computing 04/2012; 9(3):513-543.
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ABSTRACT: Negative selection and the associated r-contiguous matching rule is a popular immune-inspired method for anomaly detection problems. In recent years, however, problems such as scalability and high false positive rate have been empirically noticed. In this article, negative selection and the associated r-contiguous matching rule are investigated from a pattern classification perspective. This includes insights in the generalization capability of negative selection and the computational complexity of finding r-contiguous detectors.Natural Computing 04/2012; 8(3):613-641.
Article: Neural networks and quantum neurology: a speculative heuristic towards the formal architecture of psychism[show abstract] [hide abstract]
ABSTRACT: A new line of investigation known as quantum neurology has been born in recent years. One of its objectives is to accomplish a better explanation of psychism. It basically explains the unity of consciousness, its holistic character, and the indeterminism of its responses. How is this “phenomenological explicandum” explained in classical neurological architecture? After commenting on the properties of classical architecture, we focus on the proposal of Edelman, since we consider it as probably one of the better proposals explaining psychism. The discussion of Edelman’s proposal, from the viewpoint of the problem about the “physical support” of psychism in classical physics, allows us to evaluate the strengths of his proposal, as well as the remaining insufficiencies in his explanation. The “heuristic” way of quantum neurology offers a new approach to the “phenomenological explicandum” that does not contradict, but completes classical architecture. The discussion regarding the Hameroff–Penrose hypothesis allows us to propose that the psycho-bio-physical ontology would have an architecture with three levels (or sub-architectures) and two (or three) interface systems among them. This hypothetical architecture permits us to reflect on the production of ontologies, architectures, and functional logics (real and artificial). In any case, the new quantum neurology would suggest new formulations of the psycho-bio-physical ontology by means of the graph theory (classical neurology) and of topology (quantum neurology).Natural Computing 04/2012; 8(4):645-661.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
Mary Ann Liebert
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