Definition
Genetic Algorithm
A search heuristic that mimics the process of natural selection to generate high-quality solutions to optimization and search problems.
- In other words: Genetic algorithms are a type of evolutionary algorithm inspired by the process of natural selection.
- They are used to find approximate solutions to problems where exact solutions are difficult or impossible to compute.
- Genetic algorithms are a type of heuristic.
- They do not guarantee an optimal solution but often find good solutions in a reasonable amount of time.
Key Concepts
- Population
- A population is a collection of candidate solutions to the problem.
- Each solution in the population is called an individual or a chromosome.
- Chromosome
- A chromosome is a representation of a solution to the problem.
- It is typically encoded as a string of genes, which can be binary, integer, or real-valued.
- Fitness Function
- The fitness function evaluates how good a solution is.
- It assigns a fitness score to each individual in the population.
- Selection
- Selection is the process of choosing individuals from the population to create the next generation.
- Individuals with higher fitness scores are more likely to be selected.
- Crossover
- Crossover is a genetic operator that combines the genes of two parent individuals to create offspring.
