steady state Genetic Algorithm

The Steady State Genetic Algorithm (ssGA) differs from the generational model in that there is typically one single new member inserted into the new population at any time.
A Replacement strategy defines which member of the population will be replaced by the new offspring ( worst, Oldest or Random individual).
The better performance of the ssGA can be explained by the fact that an offspring is immediately used as a part of the mating pool making a shift towards the optimal solution possible in a relatively early phase of the optimization process.
ssGA Algorithm
Initialization [Population]
Evaluation [ Population]
Gen=0
Do
Selected_Parents = Selection[Population]
Created_Offspring= Recombination[selected_parents]
Mutation[Created_Offspring]
Population = Created_Offspring+Replacement[Population]
Evaluation[Created_Offspring]
Gen=Gen+1
Until Stop_Criterion
The main difference between the sGA and ssGA is:
Selection : in this algorithm we can mainly used tournament selection .
Crossover : we can mainly used (2X) or (UX) .
Mutation : we can use high probability for mutation because ssGA can work with high mutation value duo to the Replacement strategies.
Replacement : the main idea in ssGA is the replacement strategy