Genetic Algorithms
Learn about genetic algorithms by exploring their key concepts and functionality, and create art through evolution.
Overview
Genetic algorithms are inspired by one of nature's most fascinating processes: evolution. Imagine a child learning to ride a bicycle for the first time. In the beginning, the child tries various methods, each representing a different approach to balancing, pedaling, and steering. Each attempt provides valuable feedback—sometimes, they stay upright, and sometimes they fall. As they practice, they learn which techniques work best and which don’t, gradually refining their skills. They combine successful strategies from different attempts, occasionally experimenting with new adjustments until they master riding smoothly and confidently. This learning process mirrors the principles of a genetic algorithm. Let's dive deeper into the details of this algorithm.
The algorithm
A genetic algorithm (GA) is a search heuristic to find approximate solutions to optimization and search problems by mimicking the process of natural evolution. Genetic algorithms are particularly useful when dealing with problems where the search space is large, complex, or poorly understood.
Key concepts
Population: A collection of individuals, each representing a possible solution to the problem. The population evolves over time through iterations (known as generations).
Chromosome: A representation of an individual solution, typically encoded as a string of binary digits (0s and 1s), although other encodings (e.g., real numbers, characters) are also used depending on the problem.
Gene: A segment of a chromosome that represents a particular trait of the solution. In a binary encoding, each gene is a bit (0 or 1).
Fitness function: A function that evaluates and assigns a fitness score to each individual in the population based on how well it solves the problem at hand. The fitness score determines the probability of an individual being selected for reproduction.
Selection: The process of choosing individuals from the current population to create offspring for the next generation. The selection is typically based on fitness scores, with fitter individuals having a higher probability of being selected.
Crossover (recombination): A genetic operator that combines two parent chromosomes to produce offspring. Crossover is like mixing traits from two parents to create a new, unique combination, helping to make each generation more diverse.
Mutation: A genetic operator that ...