Going back to our example, the function could be the sum of the distances in the route.
At this point we take the scores, and each chromosome will get a relative probability to be selected as parent for the next generation.
Our main goal is to select “parents” based on their probabilities (calculated by their fitness score), breed them, and move the produced child into the next generation.
The breeding process is derivative of the problem, when we need to find a way to combine the parents into a valid solution.
Selection – Selecting pairs of parent solutions according to their Fitness value 3.
Crossover – Breeding the selected parents to produce an offspring 4.
When trying to breed parents, the first idea that comes up is to take 50% from each one.
However, in the Travelling Salesman Problem (TSP) it might lead to an invalid solution – in which each city will appear more than once. By taking the first part from the first parent, and then taking the rest of the cities according to their order of appearance on the second parent solution.
What’s the connection between evolutionary algorithms and mother nature, and how can it help solve complicated computing problems?
Wikipedia defines evolution as “a change in the heritable characteristics of biological populations over successive generations”.