Natural computing
Natural computing,, among others.
Computational paradigms studied by natural computing are abstracted from natural phenomena as diverse as
Dually, one can view processes occurring in nature as information processing. Such processes include self-assembly,
Nature-inspired models of computation
The most established "classical" nature-inspired models of computation are cellular automata, neural computation, and evolutionary computation. More recent computational systems abstracted from natural processes include swarm intelligence, artificial immune systems, membrane computing, and amorphous computing. Detailed reviews can be found in many books .[8][9]
Cellular automata
A cellular automaton is a dynamical system consisting of an array of cells. Space and time are discrete and each of the cells can be in a finite number of states. The cellular automaton updates the states of its cells synchronously according to the transition rules given
Conway's Game of Life is one of the best-known examples of cellular automata, shown to be computationally universal. Cellular automata have been applied to modelling a variety of phenomena such as communication, growth, reproduction, competition, evolution and other physical and biological processes.
Neural computation
Neural computation is the field of research that emerged from the comparison between computing machines and the human nervous system.[10]
This field aims both to understand how the
An
Evolutionary computation
Evolutionary computation
An artificial evolutionary system is a computational system based on the notion of simulated evolution. It comprises a constant- or variable-size population of individuals, a fitness criterion, and genetically inspired operators that produce the next generation from the current one. The initial population is typically generated randomly or heuristically, and typical operators are
The study of evolutionary systems has historically evolved along three main branches:
Swarm intelligence
Particle swarm optimization applies this idea to the problem of finding an optimal solution to a given problem by a search through a (multi-dimensional)
In the same vein,
Artificial immune systems
Artificial immune systems (a.k.a. immunological computation or
Viewed as an information processing system, the
Membrane computing
A generic membrane system (P-system) consists of cell-like compartments (regions) delimited by membranes, that are placed in aApplications of membrane systems include machine learning, modelling of biological processes (
Amorphous computing
In biological organisms, morphogenesis (the development of well-defined shapes and functional structures) is achieved by the interactions between cells guided by the genetic program encoded in the organism's DNA.
Inspired by this idea, amorphous computing aims at engineering well-defined shapes and patterns, or coherent computational behaviours, from the local interactions of a multitude of simple unreliable, irregularly placed, asynchronous, identically programmed computing elements (particles).[23] As a programming paradigm, the aim is to find new
Morphological computing
The understanding that the morphology performs computation is used to analyze the relationship between morphology and control and to theoretically guide the design of robots with reduced control requirements, has been used in both robotics and for understanding of cognitive processes in living organisms, see Morphological computation and .[24]
Cognitive computing
Cognitive computing CC is a new type of computing, typically with the goal of modelling of functions of human sensing, reasoning, and response to stimulus, see Cognitive computing and .[25]
Cognitive capacities of present-day cognitive computing are far from human level. The same info-computational approach can be applied to other, simpler living organisms. Bacteria are an example of a cognitive system modelled computationally, see Eshel Ben-Jacob and Microbes-mind.
Synthesizing nature by means of computing
Artificial life
Artificial life (ALife) is a research field whose ultimate goal is to understand the essential properties of life organisms [26] by building, within electronic computers or other artificial media, ab initio systems that exhibit properties normally associated only with living organisms. Early examples include
Pioneering experiments in artificial life included the design of evolving "virtual block creatures" acting in simulated environments with realistic features such as
These artificial creatures were selected for their abilities endowed to swim, or walk, or jump, and they competed for a common limited resource (controlling a cube). The simulation resulted in the evolution of creatures exhibiting surprising behaviour: some developed hands to grab the cube, others developed legs to move towards the cube. This computational approach was further combined with rapid manufacturing technology to actually build the physical robots that virtually evolved.[29] This marked the emergence of the field of mechanical artificial life.The field of synthetic biology explores a biological implementation of similar ideas. Other research directions within the field of artificial life include
Nature-inspired novel hardware
All of the computational techniques mentioned above, while inspired by nature, have been implemented until now mostly on traditional electronic hardware. In contrast, the two paradigms introduced here, molecular computing and quantum computing, employ radically different types of hardware.
Molecular computing
The first experimental realization of special-purpose molecular computer was the 1994 breakthrough experiment by Leonard Adleman who solved a 7-node instance of the
One of the most notable contributions of research in this field is to the understanding of self-assembly.[33] Self-assembly is the
Theoretical research in molecular computing has yielded several novel models of DNA computing (e.g.
Quantum computing
A quantum computer
Quantum cryptography is not based on the complexity of the computation, but on the special properties of quantum information, such as the fact that quantum information cannot be measured reliably and any attempt at measuring it results in an unavoidable and irreversible disturbance. A successful open air experiment in quantum cryptography was reported in 2007, where data was transmitted securely over a distance of 144 km.[40] Quantum teleportation is another promising application, in which a quantum state (not matter or energy) is transferred to an arbitrary distant location. Implementations of practical quantum computers are based on various substrates such as ion-traps,
Nature as information processing
The dual aspect of natural computation is that it aims to understand nature by regarding natural phenomena as information processing. Already in the 1960s, Zuse and Fredkin suggested the idea that the entire universe is a computational (information processing) mechanism, modelled as a cellular automaton which continuously updates its rules.[3][4] A recent quantum-mechanical approach of Lloyd suggests the universe as a quantum computer that computes its own behaviour,[5] while Vedral [42] suggests that information is the most fundamental building block of reality.
The universe/nature as computational mechanism is elaborated in,[6] exploring the nature with help of the ideas of computability, whilst,[7] based on the idea of nature as network of networks of information processes on different levels of organization, is studying natural processes as computations (information processing).
The main directions of research in this area are systems biology, synthetic biology and cellular computing.
Systems biology
Computational systems biology (or simply systems biology) is an integrative and qualitative approach that investigates the complex communications and interactions taking place in biological systems. Thus, in systems biology, the focus of the study is the
Another viewpoint is that the entire genomic regulatory system is a computational system, a genomic computer. This interpretation allows one to compare human-made electronic computation with computation as it occurs in nature.[44]
Genomic computer | Electronic computer | |
---|---|---|
Architecture | changeable | rigid |
Components construction | as-needed basis | from the start |
Coordination | causal coordination | temporal synchrony |
Distinction between hardware and software | No | Yes |
Transport media | molecules and ions | wires |
In addition, unlike a conventional computer, robustness in a genomic computer is achieved by various
Transport networks refer to the separation and transport of substances mediated by lipid membranes. Some lipids can self-assemble into biological membranes. A lipid membrane consists of a lipid bilayer in which proteins and other molecules are embedded, being able to travel along this layer. Through lipid bilayers, substances are transported between the inside and outside of membranes to interact with other molecules. Formalisms depicting transport networks include membrane systems and brane calculi.[48]
Synthetic biology
Synthetic biology aims at engineering synthetic biological components, with the ultimate goal of assembling whole biological systems from their constituent components. The history of synthetic biology can be traced back to the 1960s, when François Jacob and Jacques Monod discovered the mathematical logic in gene regulation. Genetic engineering techniques, based on recombinant DNA technology, are a precursor of today's synthetic biology which extends these techniques to entire systems of genes and gene products.
Along with the possibility of synthesizing longer and longer DNA strands, the prospect of creating synthetic genomes with the purpose of building entirely artificial synthetic organisms became a reality. Indeed, rapid assembly of chemically synthesized short DNA strands made it possible to generate a 5386bp synthetic genome of a virus.[49]
Alternatively, Smith et al. found about 100 genes that can be removed individually from the genome of
A third approach to engineering semi-synthetic cells is the construction of a single type of RNA-like molecule with the ability of self-replication.[50] Such a molecule could be obtained by guiding the rapid evolution of an initial population of RNA-like molecules, by selection for the desired traits.
Another effort in this field is towards engineering multi-cellular systems by designing, e.g., cell-to-cell communication modules used to coordinate living bacterial cell populations.[51]
Cellular computing
Computation in living cells (a.k.a.
Other approaches to cellular computing include developing an
See also
- Computational intelligence
- Bio-inspired computing
- DNA computing
- Natural Computing journal
- Quantum computing
- Synthetic biology
- Unconventional computing
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Further reading
This article was written based on the following references with the kind permission of their authors:
- Lila Kari, Grzegorz Rozenberg (October 2008). "The Many Facets of Natural Computing". Communications of the ACM. 51 (10): 72–83. .
- Leandro Nunes de Castro (March 2007). "Fundamentals of Natural Computing: An Overview". Physics of Life Reviews. 4 (1): 1–36. .
Many of the constituent research areas of natural computing have their own specialized journals and books series. Journals and book series dedicated to the broad field of Natural Computing include the journals Natural Computing (Springer Verlag), Theoretical Computer Science, Series C: Theory of Natural Computing (Elsevier), the Natural Computing book series (Springer Verlag), and the Handbook of Natural Computing (G.Rozenberg, T.Back, J.Kok, Editors, Springer Verlag).
- Ridge, E.; Kudenko, D.; Kazakov, D.; Curry, E. (2005). "Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments". Self-Organization and Autonomic Informatics (I). 135: 35–49. CiteSeerX 10.1.1.64.3403.
- Swarms and Swarm Intelligence by Michael G. Hinchey, Roy Sterritt, and Chris Rouff,
For readers interested in popular science article, consider this one on Medium: Nature-Inspired Algorithms