Universal Approach to Solution of Optimization Problems by Symbolic Regression

Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it impossible to determine a distance between two potential solutions and, therefore, algorithms using arithmetic operations of multiplication addition not used there. optimal the space codes. It proposed that principle small variations basic be applied as universal approach create algorithms. Small cause neighborhood solution, searched for within this neighborhood. concept inheritance property introduced. shown space, application evolution possible effective. Examples variation different presented.