Genetic Algorithms

A Genetic Algorithm (GA) is an evolutionary computation method: it uses natural selection principles (selection, crossover, mutation) to find optimal solutions for complex optimization and search problems.

Genetic Algorithms (GAs) are a powerful class of metaheuristics: they mimic biological evolution to solve problems where traditional methods fail. The process begins with a population of candidate solutions, which are iteratively evaluated using a defined fitness function. The fittest individuals are selected as 'parents' to generate the next generation via crossover (recombination) and mutation (random change) operators. This process drives the population toward an optimal or near-optimal solution over successive generations. Applications are broad, including engineering design (e.g., the 2006 NASA ST5 evolved antenna) and machine learning (hyperparameter optimization).

https://en.wikipedia.org/wiki/Genetic_algorithm