Evolutionary Computation Evolutionary Computation is, like neural networks, an example par excellence for an information processing paradigm that was originally developed and exhibited by nature and later discovered by man who subsequently transformed the general principle into computational algorithms to be put to work on computers.
Nature makes in an impressive way use of the principle of genetic heritage and evolution.
Mutually beneficial interdependencies, co-operation and even apparently altruistic behaviour can emerge solely by evolution. The investigation of those phenomena is part of research in artificial life but cannot be dealt with in this book. Genetic algorithms and evolution strategies emerged at about the same time in the United States of America and Germany. Genetic algorithms in their original form were not primarily designed for function optimisation but to demonstrate the efficiency of genetic crossover in assembling successful candidates over complicated search spaces. Genetic programming takes the idea of solving an optimisation problem by evolution of potential candidates one step further in that not only the parameters of a problem but also the structure of a solution is subject to evolutionary change.