Overview
Genetic algorithms are used to find approximate solutions to difficult optimization problems by 'evolving' a population of candidate solutions over many generations.
The Process
- Initialization: Create a random population of 'chromosomes' (potential solutions).
- Fitness Evaluation: Score each solution based on how well it solves the problem.
- Selection: Choose the best-performing individuals to be 'parents.'
- Crossover (Recombination): Combine parts of two parents to create 'offspring.'
- Mutation: Randomly alter parts of the offspring to maintain diversity.
- Repeat: The new population replaces the old one, and the process continues until a satisfactory solution is found.
Applications
- Engineering design optimization.
- Scheduling and logistics.
- Training neural network architectures (Neuroevolution).