* Create an initial population of randomly-generated individuals
* Evaluate each member’s fitness (“fitness” depends entirely on the task in hand; a member’s fitness might be how many 1s its genes contain, or how well it plays chess using its genes as “weights” for various evaluation parameters)
* Kill the bottom x% (least fit) of the population (often 50%)
* Let the fittest members breed (“reproduction”):
Choose two members for breeding (“selection”) – lots of variations here: e.g. either select two at random (“uniform”), or weight the selection according to a member’s fitness (“fitness-proportionate”)
* Breed them (“crossover” – more options here: e.g., a child can be formed by single-point crossover (the parents’ genes are swapped over at some random point along their chromosome), two-point crossover (the parents’ genes are swapped over at two random points), uniform crossover (the parents’ genes are selected bit-by-bit randomly), and weighted crossover (the parents’ genes are selected bit-by-bit randomly but weighted according to each parents’ fitness)
* Apply mutation – each gene of the child is subject to a small chance of mutating to a different allele (an allele is a possible value for a gene, in binary, the alleles are “0” and “1”) – a common mutation rate is 0.001
* The children form the new population of members
* Go to step 2 until you’ve evolved a suitably fit member or population
Crossover rate = 0: no crossover
Crossover rate = 0.7: “normal” crossover
Crossover rate = 1: crossover always applies
Mutation = 0: no mutation
Mutation = 0.001: “normal” mutation
Mutation = 0.05: high mutation
String Length: 64
Population: 10
Crossover rate: 0.9
Number of Generations: 5
Mutation: 0.001