Introduction
Genetic Algorithm are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might otherwise find in a lifetime (Salvatore Mangano, Computer Design, May 1995)
The solution that you get from a genetic algorithm is the result of the iteratively application of different stochastic operators.
Basically the strucure of Genetic Algorithm is always the same presented below
1. produce an initial population of indivuals
2. evaluate the fitness of all individuals
3. while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
use of GA
if alternate solutions take too much time or become too complicated
if there is a need for new approaches
if the problem is similar to one, that was alreadey solved by GA
if you want to get hybrid approaches
Example
We toss a fair coin 60 times and get the following initial population:
Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \color{red}\sum_{i=1}^{6}f(s_i)=34
The new population after performing selection:
We only perform a crossover for the pairs and
Crossover-points: 2 and 5
Before crossover:
After crossover:
Before applying mutation:
After applying mutation with updated fitness-values:
Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \sum_{i=1}^{6}f(s_i''')=37
Improvement of 9 percent