Genetic Algorithm vs Traditional/Classical Algorithm

Genetic Algorithm vs Traditional/Classical Algorithm

A genetic algorithm differs from a classical,Traditional  derivative-based, optimization algorithm in two ways:

• GA genetic algorithm generates a population of points in each iteration
• CA classical algorithm generates a single point at each iteration.

• GA genetic algorithm selects the next population by  Probability computation using random number generators.
• CA  classical algorithm selects the next point by deterministic computation.

Compared to traditional artificial intelligence, a genetic algorithm provides many advantages.

• GA works with  coding of Parameters set.
• CA  works themselves.

• GA is more robust and is susceptible to the presence of noise.
• CA is Less robust

• GA  genetic algorithm can provide better and more significant results while searching large multi-modal state spaces, large state spaces or n-dimensional surfaces.
• CA  provide better and less significant results while searching large multi-modal state spaces, large state spaces or n-dimensional surfaces.

Applications 

• robotics
• automotive design
•  optimized telecommunications routing
• engineering design 
• computer-aided molecular design