Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others. However, the field is more related to biological computation. Computational paradigms studied by natural computing are abstracted from natural phenomena as diverse as self-replication, the functioning of the brain, Darwinian evolution, group behavior, the immune system, the defining properties of life forms, cell membranes, and morphogenesis. Besides traditional electronic hardware, these computational paradigms can be implemented on alternative physical media such as biomolecules (DNA, RNA), or trapped-ion quantum computing devices. Dually, one can view processes occurring in nature as information processing. Such processes include self-assembly, developmental processes, gene regulation networks, protein–protein interaction networks, biological transport (active transport, passive transport) networks, and gene assembly in unicellular organisms. Efforts to understand biological systems also include engineering of semi-synthetic organisms, and understanding the universe itself from the point of view of information processing. Indeed, the idea was even advanced that information is more fundamental than matter or energy. The Zuse-Fredkin thesis, dating back to the 1960s, states that the entire universe is a huge cellular automaton which continuously updates its rules. Recently it has been suggested that the whole universe is a quantum computer that computes its own behaviour. The universe/nature as computational mechanism is addressed by exploring nature with the help of the idea of computability and studying natural processes as computations (information processing).
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 .
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 a priori. The next state of a cell is computed by a transition rule and it depends only on its current state and the states of its neighbors. 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. This field aims both to understand how the brain of living organisms works (brain theory or computational neuroscience), and to design efficient algorithms based on the principles of how the human brain processes information (Artificial Neural Networks, ANN ). An artificial neural network is a network of artificial neurons. An artificial neuron A is equipped with a function f A {\displaystyle f_{A}} , receives n real-valued inputs x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} with respective weights w 1 , w 2 , … , w n {\displaystyle w_{1},w_{2},\ldots ,w_{n}} , and it outputs f A ( w 1 x 1 + w 2 x 2 + … + w n x n ) {\displaystyle f_{A}(w_{1}x_{1}+w_{2}x_{2}+\ldots +w_{n}x_{n})} . Some neurons are selected to be the output neurons, and the network function is the vectorial function that associates to the n input values, the outputs of the m selected output neurons. Note that different choices of weights produce different network functions for the same inputs. Back-propagation is a supervised learning method by which the weights of the connections in the network are repeatedly adjusted so as to minimize the difference between the vector of actual outputs and that of desired outputs. Learning algorithms based on backwards propagation of errors can be used to find optimal weights for given topology of the network and input-output pairs.
Evolutionary computation
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