Vintage Computing

Natural calculating: A philosophy

Calculating natural-philosophy.

Oluwayomi.O.Oluwadara,

School of Technology

St Patrick's College,

8FS W1F, London

1st June 2010.

Abstract: This coin is clearly the philosophy behind the field of natural computing. It goes far to assess some basic computing architectures that are inspired by nature

Background

The computing today has provided solutions almost any task men can think of except the "world of tomorrow", the stage of ubiquity. Sequel to this, the challenge ahead is to exploit what it takes to make the computer work natural for us. Proponents of natural computer discovered that Alan Turin-day architecture Von Neumann (classic model) enclosed in the envelope of silicon can not take us for "scheduled for tomorrow" world. Following this, attention was shifted from the architecture to that of natural computing. It should be noted that this classical model has now tried to reach this "world of tomorrow" with the current trend. products of their efforts resulted in neural networks (simulation functionality of the brain), Evolutionary Computing (emulation of nature), Quantum Computing (to do with the universe) and DNA computing. These asides, searches are still ongoing on how including the current model could be abandoned classical to achieve the objectives of Natural calculating al.

In essence, computer scientists are trying to achieve the "world of tomorrow" using the current model classic in various areas, while efforts are underway to abandon the classical model for some others to achieve the goal of ubiquity.

Why Natural Computing.

Only Natural calculating appear to offer a lasting solution to the will of man to the immense desire to achieve a perfect world digitized I'll rarely see the world of tomorrow. According to De Castro (2006), the calculation is the natural terminology introduced to encompass these three approaches, named, respectively: 1) computing inspired by nature: 2) simulation and emulation of natural phenomena in computers: and 3) calculation using natural materials.

A major reason that substantiates the idea of ​​further study and research in physics calculation is not very far. It must be a solution for complex tasks that could not be easily calculable. For example, the task of making decisions of man, the task to food in the kitchen etc. Following this, the simulation of how nature solves this problem is useful: this is Natural calculating that comes in. For the sake of brevity, information can be described as natural a way of using nature to solve ambiguous problems.

The natural evolution of computing.

I would say that natural resources has evolved computer since the days of Alan Turin-Von Neumann architecture to solve complex problems at the stage of computational biology. Science, after various studies have concluded that the world of tomorrow may be obtained by the complex interaction of biology and computer science. As indicated by Lyngso (2001), computational biology is concerned with the use of computers to biological problems, most obviously in problems in evolutionary biology and molecular level. This area is also known as bioinformatics and these two terms are often used interchangeably. We can therefore infer that the combination of natural knowledge of nature and biology coupled with the computer will certainly offer an alternative to the architecture existing.

Simply put, the calculation is the natural act of building a computational model for solving problems that is inspired Nature. As indicated above, based on the current architecture, science has tried its best to calculate success based on course and that great effort, they realized the scope below:

Evolutionary-Computing

Neural-Network

Swarm- Computing.

Evolutionary Computing: It can be described as the solution method of calculation is based on the discovery the renowned biologist Charles Darwin. Eiben and Smith (2007), argued that Evolutionary Computing is the collective name for a range of technical problem solving based on the principles of biodiversity evolution, like natural selection and genetic inheritance. These techniques are becoming more widely applied to a variety of problems ranging from practical applications in industry and trade in advanced scientific research. One can deduce from the author of this book that science has adapted the results of the great Darwin to solve complex problems of today. It should also be remembered however that the experiments are conducted on the "old" classic. Derivatives and substance arising as a result of evolutionary computing involves genetic algorithm, genetic programming, evolution equipment, artificial life and artificial immune system.

Neural Network / Computer Science: Neural networks are based on the parallel architecture of the human brain. Basically, they are trained to simulate how neurons in the human body work by transmitting signals from one to another. A major difference between neurons in humans and neural networks is that the former comes naturally to take action while the latter must be formed. Mathematics discovered the learning rate for training the neural network. It has been proved that if the error result is zero or near zero, neural network has learned. As it turns out, they can also be described as a form of multiprocessor computer system with simple elements treatment with a high degree of interconnection. In addition, they received a simple scalar messages and interaction between elements of adaptation. Neural networks, because of their ability to learn (using some learning algorithms) has been applied in various human activities. Common among them are face, voice and motion detection.

Recently, Jain (2010) showed that networks neurons were also largely in synergy with other machine learning techniques and complementary to achieve improvements in the reliability, adaptability, and applicability.

Swarm Intelligence: Swarm intelligence may be compared to the scenario that the observed behavior of social insects (ants, bees, termites, etc.) are being computerized. A swarm was defined as a set of (mobile) agents which are capable of communicating directly or indirectly (by acting on their local environment) with each other, and which collectively carry a problem resolution circulated. Based on this generalized concept of a swarm, French researchers have actually been able to simulate the behavior of the termite nest building on a computer by applying very simple "algorithm stigmergic" and Deneubourg al. (1992).

We can therefore infer that swarm intelligence is a structure-based design the behavior of social insects. These social insects are unique in how these simple people together to accomplish complex tasks difficult. The idea behind this has been used to develop some algorithms that can be used to solve complex problems of man. Currently, the application of intelligence Swarm is to help troubleshoot mobile ad-hoc.

Classification of natural computing.

Although there is ongoing research in natural computing, but experts have identified three broad categories. They are computer with materials natural, simulation and emulation of good nature inspired computing and information technology inspired by nature. Note that each got different application domains.

Nature Inspired Computing (NIC): In this scenario, the solution to problems are designed after or inspired by nature. It is noted that the NE solution, probably a kind of algorithm is implemented using the architecture current. As indicated by the computer world, (2010), nature-inspired computing (NIC) is aimed at developing new techniques calculation after getting ideas from observing how nature behaves in different situations to solve complex problems. Research on open shelf new industries such as computing evolution, artificial neural networks immune system, intelligence swarm, and so on.

A subset of the primary network adapter is known as Biology Inspired Computing. On computing normal, there are significant differences in the calculation inspired by biology. The biological system appears to respond relatively slowly, but they not implement much higher level operations. To justify the efforts of researchers in this endeavor, an algorithm inspired by ant colonies that swarm intelligence exhibition has already been developed. Apart from a swarm intelligence algorithm. Also note that the NIC was also measurable success in genetic algorithms, neural networks, intelligent systems cellular automata and artificial.

As a summary, NIC-solutions which are inspired by nature are, according to the World of IT (2010); flexible they can be applied to a wide range of problems, so they can adapt in the face invisible data and able to learn, so robust it can handle incomplete data. They have decentralized control activities calculation.

Simulation and emulation of nature through the computer

As its name suggests, this branch of computer science and natural mimics nature to solve problems and tend to apply it to the computer. It simulates the solution biological complex problems and solve real life problems of their corresponding solutions. Again, De Castro (2006) stated there are two main approaches to simulation and emulation of nature in the computer: using techniques of artificial life or using tools to study the fractal geometry of nature. This simply means that one of these two applications can be taken in providing a solution to the nature of simulation to solve the complex problem.

Computers with natural materials: current Alan Turing-Von Neumann architecture has been implemented on the envelopes of silicon and has really helped. To speak frankly, computer scientists have gone so far as to try to achieve the "world of tomorrow" with a few ideas of natural computing with the existing architecture. The question that arises as a result of this approach is that, will it be possible to fully achieve the step ubuquitoisness? However, there is no news of a distinctive architecture, based on molecules (calculation of the membrane) which has negligible boundary must be in place to achieve the ultimate goal.

For this purpose, there must be a replacement for silicon based computers and I am fully agree with existing researchers who are trying to design computers with DNA (deoxyribonucleic acid) - DNA computing, RNA (ribonucleic acid) and machines that will not allow quantum effects. As indicated in Mendonca (2006), the calculation of DNA is a component particular a large field called molecular computing can be defined as the use of (bio) molecules bimolecular and operations to solve problems and perform calculations. It was presented by L. Adleman in 1994 when he solved NP-complete using DNA molecules and techniques for manipulating DNA bimolecular.

Conclusion and inference

Despite the current technology, and rapid progress in all areas, there are still problems that continue to elude scientists. From my own point of view, is a major concern - that the current standard model only being able to fully complete build tomorrow's world? Or, is it need a science to completely abandon this architecture and nature to achieve the optimal result of natural computing.

So far, science has, despite the limitations of the architecture Alan Turing, Von Neumann developed the algorithms that attempts to solve the problems are so complex, but easily resolved by nature. The application of such is evident in the neural computation, intelligence etc. swarm In conclusion, I strongly agree that the overall change in computer science with natural materials will eventually be the best mechanism to achieve the "magic" of computational biology.

References.

Of CASRO, N. (2006), Foundations Natural computing: basic concepts, algorithms and applications, [online], Boca Raton, Chapman and Hall: Available from, http://books.google.co.uk/books?hl=en&lr=&id = oi = fnd & N6iYpNVP9RgC & pg = PA1 & dq = Evoulution + of + natural + computing & ots = & sig = iiv19X9yjm y96f8Fw5CwPdMzOkW9VCOvJnm5U # V = OnePage & q & f = false [Accessed 06/08/2010].

Lyngso, RB (2001), computational biology, a submission to the Faculty of Science as an agent second assignment of partial fulfillment of requirements for the doctoral degree, University Aarhus.

Mendonca, C, (2006); review of the Fundamentals of natural computing, [online], available from http://research.cs.queensu.ca/home/akl/cisc879/2009/Fundamentals_of_natural_computing_an_overview.pdf

Eiben, AE and Smith JE (2007), Introduction to evolutionary computing, Jain, CL (2010); Progress in the design and application of neural networks, In, Neural Computing and Application;, Volume 19, p.167-168,

Deneubourg, Jean-Louis, and Guy Theraulaz Ralph Beckers. (1992): Swarm-made architectures, in: Varela, Francisco P. Bourgine (ed.), Towards a practical autonomous systems. Proceedings of the First European Conference on Artificial Life, Paris, 123-133. Cambridge: MIT Press.

About the Author

Oluwayomi.O.Oluwadara,

School of Technology,

St Patrick's College,

W1F 8FS,London

Multiple Classic Computer MCC (MCC-216) from Arcade Retro Gaming