Evolutionary algorithms

The beginning of research into evolutionary algorithms was inspired by the imitation of nature. All the living organisms live in certain environment. They have a specific genetic material containing information about them and allowing them to transfer their features to new generations. During reproduction, a new organism is created, which takes certain features after its parents. These features are coded in genes, and these are stored in chromosomes, which in turn constitute genetic material – genotype. During the transfer of features, genes become modified. Then the crossover of different paternal and maternal chromosomes occurs. Mutation often occurs additionally, which is the exchange of single genes in a chromosome. An organism is created which differs from that of its parents and contains genes of its predecessors but also has certain features specific to itself. This organism starts to live in a given environment. If it turns out that it is well fit to the environment, in other words – if the combination of genes turns out to be advantageous – it will transfer its genetic material to its offspring. The individual that is poorly fit to the environment will find it difficult to live in this environment and transfer its genes to subsequent generations.

The presented idea has been applied to solve optimization problems. It turns out that an analogous approach to numerical calculations can be proposed – using so-called

evolutionary algorithms

. The environment is defined upon the basis of the solved problem. A population of individuals constituting potential solutions of a given problem lives in this environment.

With the use of appropriately defined fitness function, we check to what extent they are adapted to the environment. Individuals exchange genetic material with each other, crossover and mutation operators are introduced in order to generate new solutions. Among potential solutions, only the best fit ones “survive”.

This chapter will discuss the family of evolutionary algorithms, i.e. the classical genetic algorithm, evolution strategies, evolutionary programming, and genetic programming.We are also going to present advanced techniques used in evolutionary algorithms. The second part of the chapter will discuss connections between evolutionary techniques and neural networks and fuzzy systems.