Computer Science · A4.3 Machine learning approaches (HL only)

A4.3.7 — Genetic Algorithms: Evolution as an Optimiser

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IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.7

📋 What You Will Learn

  • Describe the stages of a genetic algorithm: population, fitness function, selection, crossover, mutation, evaluation and termination
  • Trace one full generation of a genetic algorithm by hand
  • Explain how candidate solutions are encoded as chromosomes
  • Describe how genetic algorithms solve real optimisation problems such as the travelling salesperson problem (TSP)
  • Evaluate when a genetic algorithm is (and is not) an appropriate technique

🧬 Why This Matters Breeders never designed the fastest racehorse directly — they repeatedly bred the fastest pairs and occasionally nature threw in a surprise mutation. Genetic algorithms (GAs) do the same to solutions : keep the fittest, combine them, mutate a little, repeat — and good answers to problems too large to search exhaustively "evolve".

The Idea: Search by Simulated Evolution

  • Many real problems (routing, timetabling, design) have astronomically many candidate solutions — brute force is impossible (a 20-city tour already has ~10

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More notes on A4.3 Machine learning approaches (HL only)

  1. 01A4.3.2 — Classification: K-Nearest Neighbours and Decision Trees
  2. 02A4.3.3 — Hyperparameter Tuning and Evaluating Supervised Models
  3. 03A4.3.8 — Artificial Neural Networks: Perceptrons to Multi-Layer Networks
  4. 04A4.3.10 — Model Selection and Comparison in Machine Learning
  5. 05A4.3.5 — Association Rule Learning: Uncovering Relationships in Data
  6. 06A4.3.9 — Convolutional Neural Networks: Learning Spatial Hierarchies
  7. 07A4.3.6 — Reinforcement Learning: Agents, Rewards and Policies
  8. 08A4.3.1 — Linear Regression: Predicting Continuous Outcomes
  9. 09A4.3.4 — Clustering: Grouping Data by Similarity