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Zeile 9: |
Zeile 9: |
| Basically the strucure of Genetic Algorithm is always the same presented below | | Basically the strucure of Genetic Algorithm is always the same presented below |
| | | |
− | 1. produce an initial population of indivuals
| + | # produce an initial population of indivuals |
− | | + | # evaluate the fitness of all individuals |
− | 2. evaluate the fitness of all individuals
| + | # '''WHILE''' termination condition not met '''DO''' |
− | | + | ##select fitter individuals for reproduction |
− | 3. while termination condition not met do
| + | ##recombine between individuals |
− |
| + | ##mutate individuals |
− | select fitter individuals for reproduction
| + | ##evaluate the fitness of the modified individuals |
− | | + | ##generate a new population |
− | recombine between individuals
| + | #'''END WHILE''' |
− | | + | |
− | mutate individuals
| + | |
− |
| + | |
− | evaluate the fitness of the modified individuals
| + | |
− |
| + | |
− | generate a new population
| + | |
− |
| + | |
− | End while
| + | |
| | | |
| | | |
Version vom 24. Juni 2013, 22:04 Uhr
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
- produce an initial population of indivuals
- evaluate the fitness of all individuals
- 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