Genetic Algorithm
Generation will go here
Best chromosome in each generation will go here
This page shows how an Evolutionary Algorithm works, entering a string of text which is used as the goal the chromosomes create children and mutate based on their current text stored. They are evaluated and given a score based on how close their text is to the goal text. If their score is low they have less chance to create children and pass on their data.
Number of cycles/generations through the GA process
A Chromosome is a potential solution to a given problem, it essentially stores data and a fitness value.
The fitness is the evaluation of how good a chromosome is, It evaluates how good the chromosome is at solving the problem.
Mutation adds random changes into chromosomes, this helps them search for the goal It allows chromosomes to explore new spaces so that the search is not focused all in one area, avoiding stagnation
Crossover is an operation that splits the data from two parent chromosomes and distributes it to the children, this helps the evolution of the population
A point in the potential solution is chosen, everything left of the point from parent1 is passed down with everything on the right from parent2
Index chosen: 2
Parents: Hello World
Children: Helld Worlo
Two positions in the parent chromosomes are chosen at random, swap the middle section of the two points
Indexes chosen: 3, 7
Parents: understand importance
Children:undertannd imporrstace