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

Evolutionary Computation is a subset of algorithms that draw inspiration from the process of natural selection and evolution, making it fundamentally linked to Artificial Intelligence and Computational Intelligence. This field utilizes algorithms such as genetic algorithms, evolutionary strategies, and genetic programming to solve optimization and search problems by mimicking the processes of biological evolution, including selection, crossover, and mutation.

The techniques within Evolutionary Computation are designed to create and improve solutions over generations, leveraging concepts like fitness functions to evaluate how well a candidate solution solves a given problem. This iterative approach to problem-solving aligns closely with the goals of Artificial Intelligence, which seeks to simulate intelligent behavior in computers.

In contrast, while biological sciences may study the principles of evolution, they do not directly account for the computational methods employed in Evolutionary Computation. Mathematics and statistics provide foundational tools for algorithm analysis but do not encompass the full range of AI techniques. Lastly, psychology and behavioral science focus primarily on human behavior and cognition, which diverges from the computational and algorithmic focus of Evolutionary Computation. Thus, the most accurate classification of Evolutionary Computation is within Artificial Intelligence and Computational Intelligence.