Natural Computation-A Philosophy.

Oluwayomi.O.Oluwadara,

School of Technology,

St Patrick's College,

W1F 8FS,London

1st June 2010.

Abstract: This piece clearly highlights the philosophy behind the field of natural Computation. It goes further to evaluate some basic computational architectures that are nature inspired

Background

The present day computing has provided solutions to almost every task man can think of except for the "tomorrow world"-the stage of ubiquitousness. Sequel upon this, the challenge of the future is to harness all it takes to make natural computing work for us. Proponents of natural computing has discovered that the present day Alan Turin-Von Neumann architecture (classical model) enclosed in its silicon casing alone can not take us to the envisioned "tomorrow world". As a result of this, attention was shifted from this architecture to that of natural computing. It must be stressed that this current classical model has tried reaching this "tomorrow world" with the current trend. Products of their effort have resulted in Neural Networks (Simulating the functionality of the brain), Evolutionary Computing (Emulating the nature), Quantum Computing (has to do with the universe) and DNA computing. These asides, researches are still on going on how the current classical model could be abandoned in order to achieve al the goals of natural computing.

In essence, computer scientists are trying to achieve the "tomorrow world" using the current classical model in diverse areas, while there are on going efforts to abandon this classical model for some other ones in order to achieve the goal of ubiquitousness.

Why Natural Computing.

Only natural computing seem to proffer a lasting solution to the human desire for the overwhelming desire to achieve a perfect digitalized world which I will seldomly refer to as tomorrow world. According to De Castro (2006), natural computing is the terminology introduced to encompass these three types of approaches, named, respectively: 1) computing inspired by nature: 2) the simulation andemulation of natural phenomena in computers: and 3) computing with natural materials.

A major reason that substantiates the idea of further study and research in natural computing is not far fetched. There must be a solution for complex tasks that could not be readily computable .For example, task of making human decisions, task of making food in the kitchen e.t.c As a result of this, the simulation of how nature solves this kind of problem is helpful: this is where natural computing comes in. For the sake of brevity, natural computing can be described as a way of employing nature to solve ambiguous problems.

The Evolution of Natural computing.

I would say that natural computing has evolved from the era of Alan Turin- Von Neumann architecture for solving complex problems to the stage of Computational Biology. Scientist, after various researches concluded that the tomorrow world could be achieved by the complex interaction of Biology and Computer. As indicated by Lyngso (2001), computational biology is concerned with the use of computers for biological problems, most prominently problems in evolutionary and molecular biology. This area is also referred to as bioinformatics and these two terms are often used interchangeably. It can therefore be inferred that the combination of natural knowledge from nature-Biology coupled with computing will definitely provide an alternative to the existing architecture.

Simply put, natural computing is the act of building a computational model for problem solving that is nature inspired. As stated earlier, based on the current architecture, science has tried its hardest to compute naturally and based on this great effort, they have achieved success in the below application areas:

-Evolutionary Computing

-Neural Computing

-Swarm Computing.

Evolutionary Computing: This can be described as the computational solution method that is based on the finding of the renown biologist-Charles Darwin. Eiben and Smith (2007), claimed that Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance.  These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading edge scientific research. One can infer from the author of this book that science has adapted the findings of the great Darwin to solve complex present day problems. It must also be recalled however that this experimentations are being carried out on "old" classical model. Derivatives and merits that arose as a result of evolutionary computing involves genetic algorithm, genetic programming, evolutionary hardware, artificial life and artificial immune system.

Neural Network /Computing:Neural networks are based on the parallel architecture of human brain. Basically, they are formed to simulate how the neurons in the human body work by transmitting signals from one to the other. A major difference between the human neurons and neural networks is that the former just take action naturally while the latter has to be trained. Mathematics has discovered the learning rate of training the neural network. It has been proved that if the error result is zero or so close to zero, neural network has learnt. As it turns out to be, they can also be described as some form of multiprocessor computer system with simple processing elements with a high degree of interconnection. Plus, they have got a simple scalar messages and adaptive interaction between elements. Neural Networks, because of their ability to learn (using certain learning algorithms) has been applied in diverse field in human endeavors. Common among them are face, voice and motion detection.

Recently, Jain (2010) proved that neural networks have also been widely synergized with other machine learning and complementary techniques to achieve improvements in robustness, adaptivity, and applicability.

Swarm Intelligence: Swarm intelligence can be likened to the scenario whereby the behavioral attitudes of social insects (ants, bees, termites e.t.c) are being computerized. A swarm has been defined as a set of (mobile) agents which are liable to communicate directly or indirectly (by acting on their local environment) with each other, and which collectively carry out a distributed problem solving. Based on this generalized concept of a swarm, French researchers have actually been able to simulate the termite's nest-building behavior on a computer by applying a very simple "stigmergic algorithm"Deneubourg et al. (1992).

It can therefore be inferred that swarm intelligence is a design framework based on social insect behavior. These social insects are unique in the way these simple individuals cooperate to accomplish complex difficult tasks. The idea behind this fact has been used to design certain algorithms that can be used to solve human complex problems. Currently the application of swarm intelligence is helping in resolving issues in mobile ad-hoc networks.

Classification of Natural Computing.

Although there are on-going research in natural computation, but experts has identified three major classes. They are computing with natural materials, simulation and emulation of natured inspired by computing and computing inspired by nature. It must be noted that each of them has gotten different application areas.

Nature Inspired Computing (NIC): In this scenario, solution to problems are designed after, or inspired by nature. It must ne noted that the solution, probably some kind of algorithm is implemented using the current architecture. As Indicated by world of computing, (2010), Nature Inspired Computing (NIC) is one that aims to develop new computing techniques after getting ideas by observing how nature behaves in various situations to solve complex problems. Research on NIChas opened new branches such as evolutionary computation, neural networks, artificial immune systems, swarm intelligence, and so on.

A primary subset of NIC is known as Biology Inspired Computing. Relative to normal computing, there are significant differences in biology inspired computing. The biological system seems to respond quite slowly but they do implement much higher-level operations. To justify the effort of researchers in this endeavor, an algorithm, inspired by ant colonies that exhibit swarm intelligence has already been developed. Apart from swarm intelligence algorithm. It must also be noted that NIC has also made measurable success in Genetic algorithms, Neural Networks, Artificial Intelligence systems and cellular automata.

As a recap, NIC-solution that are nature inspired are; according to World of Computing (2010); flexible that they can be applied to wide range of problems, so adaptable that they can deal with unseen data and capable of learning, so robust that they can handle incomplete data. They have decentralized control of computational activities.

Simulation and Emulation of nature by means of Computing

As the name implies, this branch of natural computing imitates the way nature solve problems and tend to apply computing to it. It simulates the biological solution to complex problems and solves real life problems by their corresponding computing solutions. Again, De Castro (2006) stated that there are two main approaches to the simulation and emulation of nature in computers: by using artificial lifetechniques or by using tools for studying the fractal geometry of nature. This simply means that either of those two applications can be adopted in providing a nature-simulated solution to solve complex problem.

Computing with Natural Materials: The current Alan Turing-Von Neumann architecture has been implemented on silicon casings and has really helped a lot. Frankly speaking, computer scientists have gone as far as trying to achieve the "tomorrow world" by using some ideas of natural computing with the existing architecture. The question that arises as a result of this endeavor is that, will it be possible to fully attain the stage of ubuquitoisness? However, it is no news that a separate architecture, based on molecules (membrane computing) that has negligible limit need to be in place to achieve the ultimate goal.

To this effect, there must be a replacement for the silicon based computers and I totally concur with current researchers that 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), DNA computing is one particular component of a large field called molecular computing that can be broadly defined as the use of (bio) molecules and bimolecular operations to solve problems and to perform computation. It was introduced by L. Adleman in 1994 when he solved an NP-complete problem using DNA molecules and bimolecular techniques for manipulating DNA.

Conclusion and Inference

Despite the current technology, and rapid advances in every field, there are still some problems that continue to elude scientists. From my own angle, a major concern is -will the current classical model alone be able to completely achieve the tomorrow world? Or, is there need for science to completely abandon this architecture and build from the nature to achieve the optimal result of natural computing.

So far, science has despite the limits of the Alan Turing-Von Neumann architecture, developed algorithms that attempts to solve problems that are so complex but readily solve by nature. The application of such is evident in neural computing, swarm intelligence e.t.c. In conclusion, I strongly concur to the fact that the overall shift to computing with natural materials will eventually be the best mechanism to achieving the "magic" of computational biology.

References.

De Casro, N. (2006), Fundamentals of natural computing: basic concepts, algorithms, and application, [online], Boca Raton, Chapman and Hall: Available from, http://books.google.co.uk/books?hl=en&lr=&id=N6iYpNVP9RgC&oi=fnd&pg=PA1&dq=Evoulution+of+natural+computing&ots=iiv19X9yjm&sig=y96f8Fw5CwPdMzOkW9VCOvJnm5U#v=onepage&q&f=false [Accessed:8/6/2010].

Lyngso, R.B, (2001), Computational Biology, A dissertation presented to the faculty of science as a second mandatory assignment in partial fulfillment of the requirements for the PhD degree, University of Aarhus.

Mendonca, C, (2006); Review of Fundamentals of Natural computing, [Online], Available from http://research.cs.queensu.ca/home/akl/cisc879/2009/Fundamentals_of_natural_computing_an_overview.pdf

Eiben, A.E and Smith, J.E, (2007); Introduction to Evolutionary Computing,Jain,C.L, (2010); Advances in design and application of neural networks , In; Neural Computing And Application;, Volume 19, p.167-168,

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

By : Oluwayomi Oluwadara - About the Author:

Oluwayomi.O.Oluwadara,

School of Technology,

St Patrick's College,

W1F 8FS,London