4.3 The method: evolutionary computation. EC is a computational intelligence technique inspired from natural evolution. An EC algorithm starts with creating a population consisting of individuals that represent solutions to the problem. The first population could be created randomly or fed into the algorithm.

Genetic algorithms (GAs) were inspired by evolution, including the concepts of mutation, natural selection, inheritance, and crossover.

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.

The adaptive theory of the code evolution postulates that the structure of the genetic code was shaped under selective forces that made the code maximally robust, i.e., minimize the effect of errors on the structure and function of the synthesized proteins.

Evolutionary computation techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strive for survival. The best known algorithms in this class include genetic algorithms, evolutionary programming, evolution strategies, and genetic programming.

Evolutionary algorithms is a sub-field of evolutionary computing. Evolution strategies (ES, see Rechenberg, 1994) evolve individuals by means of mutation and intermediate or discrete recombination. ES algorithms are designed particularly to solve problems in the real-value domain.

Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection.