# A Brief Introduction to Genetic Algorithms

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About *A Brief Introduction to Genetic Algorithms:*

Excerpt from introduction:

The idea behind GA´s is to extract optimization strategies nature uses successfully - known as Darwinian Evolution - and transform them for application in mathematical optimization theory to find the global optimum in a defined phase space.

One could imagine a population of individual "explorers" sent into the optimization phase-space. Each explorer is defined by its genes, what means, its position inside the phase-space is coded in his genes. Every explorer has the duty to find a value of the quality of his position in the phase space. (Consider the phase-space being a number of variables in some technological process, the value of quality of any position in the phase space - in other words: any set of the variables - can be expressed by the yield of the desired chemical product.) Then the struggle of "life" begins. The three fundamental principles are

Selection

Mating/Crossover

Mutation

The idea behind GA´s is to extract optimization strategies nature uses successfully - known as Darwinian Evolution - and transform them for application in mathematical optimization theory to find the global optimum in a defined phase space.

One could imagine a population of individual "explorers" sent into the optimization phase-space. Each explorer is defined by its genes, what means, its position inside the phase-space is coded in his genes. Every explorer has the duty to find a value of the quality of his position in the phase space. (Consider the phase-space being a number of variables in some technological process, the value of quality of any position in the phase space - in other words: any set of the variables - can be expressed by the yield of the desired chemical product.) Then the struggle of "life" begins. The three fundamental principles are

Selection

Mating/Crossover

Mutation