Natural Computing Algorithms

The book is divided into eight main parts, each of which provides an integrated discussion of a range of related natural computing algorithms.
The first part covers a family of algorithms which are inspired by processes of evolution (evolutionary computing) as well as introducing pivotal concepts in the design of natural computing algorithms such as choice of representation, diversity generation mechanisms, and the selection of an appropriate fitness function.The second part illustrates a selection of algorithms which are inspired by the social behaviour of individuals (social computing) ranging from flocking behaviours to the food foraging behaviours of several organisms, including bats, insects and bacteria.
The third part introduces a number of algorithms whose workings are inspired by the operation of our central nervous system (neurocomputing).
The fourth part of the book discusses optimisation and classification algorithms which are metaphorically derived from the workings of our immune system (immunocomputing).
The fifth part provides an introduction to developmental and grammatical computing, where the creation of a model or structure results from a development process which is typically governed by a set of rules, a ‘grammar’.
Physical computing is described in the sixth part of the book, with the primary emphasis being placed on the use of quantum inspired representations in natural computing.
Two emerging paradigms in natural computing, chemical computing and plant inspired algorithms are introduced in part seven of the book.
The closing chapter of the book looks towards the future of natural computing algorithms.