# 3. Genetic algorithms

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/read/ga-core

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/read-chapter/ga-core/

Candidate → fitness → encoding → selection → crossover → mutation → two complete generations.

1. [GA meaning, biological idea and every basic term](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--meaning/)
2. [Encoding: binary, real values and permutations](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--encoding/)
3. [Roulette selection, cumulative intervals and alternatives](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--selection/)
4. [Crossover and mutation: four complete worked variants](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--operators/)
5. [GA architecture, two-generation flow, constraints and theory](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--algorithm/)
6. [Two generations with given selection, cut and mutation](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--two-generations/)
7. [Two generations including roulette selection](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--roulette-generations/)
8. [GA minimisation: transform cost before roulette selection](https://deep-learning-study-room.vercel.app/read/is2/tee/read/ga-core--minimisation/)