Genetic Algorithms

Overview

Genetic algorithms are inspired by one of nature's most fascinating processes: evolution. Imagine a child learning to ride a bicycle for the first time. In the beginning, the child tries various methods, each representing a different approach to balancing, pedaling, and steering. Each attempt provides valuable feedback—sometimes, they stay upright, and sometimes they fall. As they practice, they learn which techniques work best and which don’t, gradually refining their skills. They combine successful strategies from different attempts, occasionally experimenting with new adjustments until they master riding smoothly and confidently. This learning process mirrors the principles of a genetic algorithm. Let's dive deeper into the details of this algorithm.