Intermediate · Research
Selection
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Choosing individuals from a population for reproduction in genetic algorithms.
Technical Definition
Choosing individuals from a population for reproduction in genetic algorithms.
How it works
In genetic algorithms, selection is the process of choosing parent solutions from the current population to create the next generation. Individuals with better fitness scores (meaning they are closer to the desired solution) have a higher probability of being selected. This ensures that favorable traits are propagated, driving the algorithm towards optimal solutions.
Related Concepts
- Crossover — Crossover, or recombination, is a genetic operator in evolutionary computation that combines genetic information from two parent solutions to create new offspring solutions.
- Evolutionary computation — A family of optimization algorithms that mimic biological evolution to solve problems.
- Genetic algorithm (GA) — An optimization technique inspired by natural selection, using processes like mutation and crossover to find solutions to complex problems.