Immunity-Based Hybrid Learning Methods For Function Approximation

The immune system is highly distributed, highly adaptive, self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. From a computational viewpoint, the immune system has much to offer by way of inspiration. Recently there has been growing interest in the use of the natural immune system as inspiration for the creation of novel approaches to computational problems; this field of research is referred as Immunological Computation (IC) or Artificial Immune Systems (AIS).Over the years, biology has provided a rich source of inspiration for many different people in many different ways ranging from designing aircraft wings to bulletproof vests. Biology has also been used as a source of inspiration for computation problems, which can be classified as biologically motivated computing. This is different from computationally motivated biology, where computing provides the source and inspiration for models in biology. There has been much work done on the use of biological metaphors, for example neural networks, genetic algorithms and genetic programming. Recently, there has been increasing interest in using the natural immune system as a metaphor for computation in a variety of domains.