Genetic Algorithms Simplified: A Step-by-Step Example for Beginners

Author(s): Linh V Nguyen

Originally published on Towards AI.

Unraveling Nature-Inspired Optimization to Build Your First Genetic Algorithm

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Genetic Algorithm (GA) is an evolutionary computation inspired by Darwin’s theory of natural selection. Its basic principle is to mimic natural selection and reproduction while searching for optimal solutions. Imagine we’re trying to bake the perfect chocolate cookie. We’ve got the ingredients but are still determining the exact proportions. How could we figure it out? Try a few different recipes, taste them, and make minor tweaks to make them even yummier. That is the general idea of how genetic algorithms work!

Chromosomes (or genotype): An individual chromosome carries a collection of genes from its parents, representing a potential solution. For example, a simple chromosome can be written as a binary string: 101011110, where each bit is a single gene.

Population: Unlike traditional algorithms that work with a single solution, GAs maintain a whole population of solutions or a collection of chromosomes. It’s like having an entire cookbook of recipes instead of just one. This population of chromosomes allows the current generation to explore multiple possibilities simultaneously until they evolve and are replaced by the new generation.

A population of three chromosomes for a generation (Image by Author)

Fitness Function: How do we know… Read the full blog for free on Medium.

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Published via Towards AI