Simple Generational Genetic Algorithm Procedure
- Choose the initial population of individuals
- Evaluate the fitness of each individual in that population
- Repeat on this generation until termination (time limit, sufficient fitness achieved, etc.):
- Select the best-fit individuals for reproduction
- Breed new individuals through crossover and mutation operations to give birth to offspring
- Evaluate the individual fitness of new individuals
- Replace least-fit population with new individuals
Read more about this topic: Genetic Algorithm
Famous quotes containing the words simple and/or genetic:
“Young children make only the simple assumption: “This is life—you go along....” He stands ready to go along with whatever adults seem to want. He stands poised, trying to figure out what they want. The young child is almost at the mercy of adults—it is so important to him to please.”
—James L. Hymes, Jr. (20th century)
“What strikes many twin researchers now is not how much identical twins are alike, but rather how different they are, given the same genetic makeup....Multiples don’t walk around in lockstep, talking in unison, thinking identical thoughts. The bond for normal twins, whether they are identical or fraternal, is based on how they, as individuals who are keenly aware of the differences between them, learn to relate to one another.”
—Pamela Patrick Novotny (20th century)