Simple Genetic Algorithm (SGA)

**Prerequisite – **Genetic Algorithm

**Introduction :**

Simple Genetic Algorithm (SGA) is one of the three types of strategies followed in Genetic algorithm.

- SGA starts with the creation of an initial population of size N.
- Then, we evaluate the goodness/fitness of each of the solutions/individuals.

After that, the convergence criterion is checked, if it meets then we converge the algorithm otherwise go for the next steps –

- Select Np individuals from the previous population.
- Create the mating pool randomly.
- Perform Crossover.
- Perform Mutation in offspring solutions.
- Perform inversion in offspring solutions.
- Replace the old solutions of the last generation with the newly created solutions and go to step (2).

**Important Parameters while solving problems in Simple Genetic Algorithm : **

- Initial Population N
- Mating pool size Np
- Convergence threshold
- Crossover
- Mutation
- Inversion

**Features :**

- Computationally Expensive
- Biased towards more highly fit individuals
- Performs well when the initial population is large enough.

**Applications :**

- Learning robot behaviour using Simple Genetic Algorithm.
- In the finance industry.
- Neural networks.
- Soft computing Applications like Fuzzy logic, Neurocomputing.
- Optimization problems.