Nature That Breeds Solutions

For at least half a century now, the field of natural computing has grown in popularity. This popularity is largely driven by the successful use of nature-inspired approaches, such as evolutionary algorithms, artificial neural networks, swarm intelligence algorithms, artificial immune systems and many others, in solving various problems. These techniques have proven their important role on many practical implementations, and tend to improve computational efficiency at the cost of its quality – a trade-off that is necessary due to the exponential growth of the problem space for many real world applications today, where there is no known feasible exact method to find solutions. Before the emergence of these techniques, computer scientists and engineers often devised their solutions by relying on the input from human intelligence. Following the conventional artificial intelligence methods, they have to first design the ‘intelligence’ based on their thoughts and judgements, and thereafter get the computer to automate their thinking process in a logical way to solve problems. The major drawback with this kind of conventional method is the usual linearity of human thinking proABSTRACT