Genetic Algorithm

Introduction

Genetic Algorithms (GAs) are powerful optimization and search techniques inspired by the process of natural selection. They are commonly used to solve complex and high-dimensional problems where conventional optimization methods are inefficient or fail altogether. This course offers a structured and practical introduction to Genetic Algorithms, balancing theoretical understanding with hands-on implementation.

Participants will learn the core concepts of Genetic Algorithms, including population-based search, representation techniques, and selection strategies. The course covers essential genetic operators such as crossover and mutation, along with effective fitness function design to guide optimal solution discovery. Learners will implement Genetic Algorithms using Python, explore hybrid GA approaches, and apply their knowledge through real-world case studies and a project, enabling them to confidently use GAs for solving practical optimization problems.