Adaptive Heuristic ‐ Genetic Algorithms

Genetic Algorithm (GA) is a search-based optimization technique that is based on genetics and natural selection principles. It's routinely used to find optimal or nearoptimal solutions to problems that would take an eternity to solve otherwise. It's commonly utilized to tackle optimization problems in research and machine learning.
John Holland first brought GAs to the public, and produced a theoretical analysis based on a schema, which became known as the schema theorem. He was trying to figure out how likely it is for a schema to survive from one generation to the next, as well as how many schema are likely to be present in the next.
Holland's Schema Theorem is a step in the right direction for researchers trying to find out the mathematics behind how genetic algorithms function. The Schema Theorem has undergone several adjustments and suggestions over the past year in order to make it more generic. We won't go into the mathematics of the Schema Theorem in this section; instead, we'll aim to acquire a basic understanding of what the Schema Theorem is. According to the schema theorem, a schema with above-average fitness, a short defining length, and a lower order is more likely to withstand crossover and mutation.