What Is the Genetic Algorithm?

The generic algorithm uses three main types of rules at each step to create the next generation from the current population:
+ Selection rules select the individuals, called parents, that contribute to the population at the next generation.
+ Crossover rules combine two parents to form children for the next generation.
+ Mutation rules apply random changes to individual parents to form children.


Standard Algorithm:
- Generates a single point at each iteration. The sequence of points approaches an optimal solution.
- Selects the next point in the sequence by a deterministic computation.
Genetic algorithm:
- Generates a population of points at each iteration. The population approaches an optimal solution
- Selects the next population by computations that involve random choices.


Some Genetic Algorithm Terminology:

Fitness Functions
Individuals Populations and Generations
Fitness Values and Best Fitness Values
Parents and Children


Algorithm

1. The algorithm begins by creating a random initial population.
2. The algorithm then creates a sequence of new population( generations). At each step, the algorithm uses the individuals in the current generation to create the next generation. To create the new generation, the algorithm performs the following steps:
a. Scores each member of the current population by computing its fitness value.
b. Scales the raw fitness scores to convert them into a more usable range of values
c. Select parents based on their fitness
d. Produces children from the parents. Children are produced either by making random changes to a single parent - mutation, or by combining the vector entries of a pair of parents - crossover.
e. Replaces the current population with the children to form the next generation.
3. The algorithm stops when one of the stopping criteria is met.

[Stopping Conditions for the Algorithm]
a. Generations -- The algorithm stops when the number of generations reaches the value of Generations.
b. ime limit -- The algorithm stops after running for an amount of time in seconds equal to Time limit.
c. Fitness limit -- The algorithm stops when the value of the fitness function for the best point in the current population is less than or equal to Fitness limit
d. Stall generations -- The algorithm stops if there is no improvement in the objective function for a sequence of consecutive generations of length Stall generations.
e. Stall time limit -- The algorithm stops if there is no improvement in the objective function during an interval of time in seconds equal to Stall time limit.