[bibshow file=my-publications.bib show_links=1 format=custom-ieee template=custom-bibshow highlight=”P. Sequeira”]

**Genetica.NET**

In general terms, GP allows a population of *candidate programs* to change over time by means of operators inspired from natural evolution such as *selection*, *mutation* and *crossover*. The evolutionary process is guided by a *fitness function* that assesses how fit a program is (usually its output) in regard to some external objective function.

Works that used this code: [bibcite key=sequeira2018cogsci]

*Features*

- Creation of programs as
*mathematical expressions***Terminals:**constant and variables**Functions:**arithmetic functions, sine, cosine, min, max, log, exponentiation and ‘if’ conditional operator

*Genetic operators***Selection:**tournament, roulette wheel (even and uneven selectors), stochastic**Crossover:**uniform, one-point, sub-tree, context-preserving, stochastic**Mutation:**point, sub-tree, hoist, shrink, swap, simplify, fitness simplify, stochastic**Generation:**full-depth, grow, stochastic

- Population class implementing a standard steady-state
*GP evolutionary procedure* - Rank (linear and non-linear)
*fitness functions* - Measure the
*similarity*between two programs**Similarity measures:**value (according to the range of variables), primitive, leaf, sub-program, sub-combination, prefix and normal notation expression edit, tree edit, common region, average

*Conversion*of programs to/from strings in normal and prefix notation- Program
*simplification*to remove redundancies and evolutionary noise *Visual instruments*(trees) to analyze the structure of sets of programs (*e.g.*, a population):- Information, symbol, ordered symbol, sub-program

*Related Publications*

[/bibshow]