SOFT COMPUTING
Soft computing is the use of approximate calculations to provide solutions that are not so precise yet are usable, to complex computational problems. Soft computing deals with approximate models and gives solutions to real-life problems that are complex in nature. Unlike hard computing, soft computing tolerates imprecision, uncertainty, partial truth, and approximations. It is used where the problem has not been adequately specified for the use of conventional math and computer techniques.
Soft Computing is an umbrella term that thoroughly studies the simulation of reasoning, human nervous system and evolution in different fields. Fuzzy Logic is a technique that understands the vagueness of a solution and presents the solution with a degree of vagueness which is practical to human decision. It is widely applied in several applications of Artificial Intelligence for reasoning. Neural Network is a network of artificial neurons, inspired by biological network of neurons, that uses mathematical models as information processing units to discover patterns in the data which is too complex to notice by humans.
Evolutionary Computation is a family of optimization algorithms that are inspired by biological evolution, such as Genetic Algorithm, survival of creatures such as Particle Swarm Intelligence, Ant Colony Optimization, Artificial Bee Colony optimization etc. or any biological processes. Genetic Algorithm is a search-based optimization technique which is based on the principles of Genetics and Natural Selection. It is usually used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is mostly used to solve optimization problems, in research, and in machine learning.
A problem can be solved most effectively by using Neural Networks, Fuzzy Logic and Evolutionary Computation in combination rather than exclusively.
