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

Evolutionary Algorithms are inspired by the process of natural evolution and typically involve mechanisms that simulate biological evolution to solve optimization problems. The primary components of these algorithms include reproduction, mutation, and selection.

Reproduction refers to the process where individuals (solutions) are combined to produce new offspring. This is a fundamental mechanism, akin to genetic merging in biological systems, essential for maintaining genetic diversity in the population.

Mutation introduces random changes to the offspring to ensure variability. This process helps to prevent the algorithm from converging to a local optimum by exploring new areas of the solution space.

Selection is the mechanism through which individuals are chosen to continue to the next generation based on their fitness. This mimics the survival of the fittest concept, allowing better-performing solutions to propagate their genes into future generations.

In contrast, intuition is not a mechanism defined within the framework of Evolutionary Algorithms. While human intuition can guide the design of these algorithms or influence their parameters, it does not represent a systematic or algorithmic step involved in the evolutionary process. Therefore, it is accurate to identify intuition as the component that is not typically part of Evolutionary Algorithms.