Similar to Genetic Algorithms.
The emphasis here is on mutation and there is no crossover.
Every individual mutates, doubling the population.
The selection/survival operator tends to be Tournament Selection.
Tournament Selection
Each member of the population is compared with a fixed number of other individuals. For each comparison, the individual is awarded a point if it’s fitness is better than the opponent. The population is then reduced back to its original size by retaining those with the highest score.
Pseudo-Code
Input: Population size, number of generations
and Fitness Function
1) Create the initial population
2) For i = 1 to number of generations
3) Mutate the population
4) Apply Tournament Selection
5) End For
Output: Return the best individual
Genetic Programming
It extends Genetic Algorithms.
We evolve computer programs by Natural Selection.
Symbolic Regression is a type of Genetic Programming, which represents a mathematical expression as a tree structure.
