- Encoding of the problem in binary string.
- Random generating of a population. This one includes a genetic pool representing a group of possible solution.
- Reckoning of a fitness value for each subject. It will directly depend on the distance of optimum.
- Selection of the subjects that will mate according to their share in the population global fitness.
- Genomes crossover and mutations.
- And then start again from point 3.
For now I need to try some example that related with GA. My focus now is understanding the GA operation itself and to know how GA can help me using optimization method to find my solution. Example each node that I need to force to sleep mode will represent by a bit. Bit 1 represent a node in active mode and bit 0 will represent node that in sleeping mode. With this all bit representative can build one chromosome. With this chromosome it can start with GA operation that will come with new offspring. This new offspring can be test with a formula or simulation that can give result where it was good chromosome or bad chromosome.