Evolutionary algorithms are stochastic optimization techniques based on the
principles of natural evolution and Genetic Programming (GP) belongs to this family .

In recent years the study of GP systems has been extended to phenotypic aspects while in previous phase it was mainly focused on genotypic and syntactic aspects.

Phenotype or semantic is utilized with the aim of optimizing the capacity of GP algorithms to explore the solution space in an effective way, classifying similar individuals and exploring new semantic areas, increasing the probability to find an optimal solution and to escape local optimum.

Currently semantic GP is strictly related to the evaluation of individual's behavior in the candidate population: this kind of evaluation is mainly obtained through the fitness function itself.

This work introduces a new way of measuring semantic similarity between individuals that is more independent from the fitness itself, allowing a fair comparison even when the finesses values involved are very far away from each other. This new measure enable a new series of techniques to be used to tackle the open problems in GP, like bloat and over-fitting, and also targeting the phenotype's variety preservation thereby enhancing performances. Preliminary results will be provided.

A new theoretical GP algorithm based on this new semantic measure it is also introduced showing the potential advantages. Very early results coming from a first naive implementation show interesting insight on this potential comparing with others on the cutting edge algorithms.