Blog

In the diverse world of Artificial Intelligence (AI), Genetic Algorithms (GAs) are a search heuristic that are inspired by Charles Darwin’s theory of natural evolution. This evolutionary algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction to produce offspring for the next generation.

The primary function of Genetic Algorithms is optimisation, which they accomplish through a natural, evolutionary approach. The procedure starts with a set of solutions (referred to as a “population”) for an optimisation problem. Solutions from one population are taken and used to form a new population, in a manner that is hoped to be better than the previous population.

The notion of “survival of the fittest” is essential in Genetic Algorithms. Chromosomes, which are potential solutions to the problem, compete for resources and reproduction. Over time, a population of good solutions evolves.

The building blocks of GAs include chromosomes (or the genome), which are usually represented as strings of binary numbers. Each chromosome represents a solution and has a set of parameters to define a proposed solution. Each solution’s fitness is calculated by a fitness function to assess its quality or suitability.

Genetic Algorithms involve three main types of rules for evolving the solutions in the population: selection rules (selects the individuals, or parents, that contribute to the population at the next generation), crossover rules (combines two parents to form children for the next generation), and mutation rules (applies random changes to individual parents to form children).

Genetic Algorithms have been successfully applied in many fields such as search optimisation, machine learning, data mining, and image processing. They have also proven valuable in addressing complex problems where other algorithms struggle to find optimal solutions.

While Genetic Algorithms offer powerful optimisation and search capabilities, their effectiveness can greatly depend on the setting of the genetic parameters and the design of the fitness function. Hence, it requires a good understanding of the problem domain and careful design.

In the upcoming articles, we’ll explore other AI terminologies like Expert Systems, and Robotics. We’ll explain what they are, how they work, and why they’re important. By the end of this series, you’ll have a solid understanding of the key concepts and ideas behind AI, and you’ll be well-equipped to explore this exciting field further.