Computational Intelligence (CI) is a branch of Artificial Intelligence that emphasizes creating intelligent systems by simulating natural processes like learning and evolution. It uses heuristic algorithms to approach problems that are complex, unstructured, or have incomplete data.
Imagine trying to solve a complex jigsaw puzzle. Computational Intelligence is like having a special friend to help you, who can imagine the final picture even if some pieces are missing, and can guide you to put the pieces together. This friend learns from how puzzles are solved, and gets better over time, just like how animals and humans learn.
Computational Intelligence is a set of nature-inspired computational methodologies and approaches that enable both humans and machines to learn from data, evolve methodologies for optimization, handle fuzzy information, learn from the environment and become more intelligent over time.
Three primary methodologies in Computational Intelligence are Neural Networks, Fuzzy Systems, and Evolutionary Computation.
Neural Networks are computing systems with interconnected nodes, inspired by our biological neural networks, that are designed to simulate the way the human brain analyzes and processes information. They are the foundation of many modern machine learning algorithms.
Fuzzy Systems are a collection of mathematical tools for dealing with imprecise or subjective information. They’re designed to solve problems in the same way that humans do: by considering all available information and making the best decision given what’s known.
Evolutionary Computation is inspired by biological evolution, like inheritance, mutation, selection, and crossover. Algorithms based on these principles, such as genetic algorithms, evolve solutions to optimization and search problems over generations.
Importantly, Computational Intelligence relies on heuristic algorithms. These algorithms learn and improve over time, allowing them to approach complex problems that do not have clear, deterministic solutions, or when data may be unstructured or incomplete. CI methodologies tend to be tolerant of imprecision, uncertainty, partial truth, and approximation, which makes them highly suitable for real-world complex systems.