Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

Genetic Algorithms are characterized primarily as a search heuristic that mimics the process of natural evolution. This approach is inspired by the principles of natural selection and genetics, where the idea is to evolve solutions to optimization and search problems over successive generations.

The process typically involves a population of candidate solutions that undergo selection, crossover (recombination), and mutation, akin to biological evolution. The fittest individuals are more likely to be selected for reproduction, enabling the population to improve toward an optimal solution over time. This mimicking of natural processes enables Genetic Algorithms to effectively explore large and complex search spaces, making them particularly useful in scenarios where traditional optimization methods may struggle.

Other options, while they represent different concepts, do not capture the essence of Genetic Algorithms. For instance, supervised learning involves training a model with labeled data, which is distinct from the evolutionary processes of Genetic Algorithms. Neural networks represent a different domain in machine learning focused primarily on function approximation and pattern recognition. Formal symbol manipulation systems are more aligned with logical reasoning and rule-based processing rather than the heuristic and stochastic nature of Genetic Algorithms. Thus, the correct characterization emphasizes their evolutionary and heuristic nature.