Aim: To end up with a healthy population from a basic set of genome
Natural Selection:
We follow the process of natural selection to generate high-quality ‘population’.
Evolution:
- We use bio-inspired operators such as mutation, crossover and elite-selection to ensure that the quality of the population improves after each generation.
- The evolution starts from a population of randomly generated individuals, and in an iterative process, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual’s genome is modified (recombined and possibly randomly mutated) to form a new generation.
- The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.