Section outline

  • ·         Overview: Genetic Algorithms (GAs) are optimization methods that evolve solutions by mimicking natural selection. This lesson teaches how GAs work and why they’re useful for creating rules or designs that aren’t easily found by conventional programming.

    • Micro-Topic 7.1: What Are Genetic Algorithms? – Goal: Explain the concept of a genetic algorithm as “survival of the fittest” applied to computing.

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    • Micro-Topic 7.2: Chromosomes, Genes, and Fitness – Goal: Show how solutions are encoded and evaluated in a GA.

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    • Micro-Topic 7.3: Selection and Crossover – Goal: Describe how genetic algorithms breed new solutions from existing ones.

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    • Micro-Topic 7.4: Mutation and Maintaining Diversity – Goal: Explain the mutation step and why diversity is important in GAs.

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    • Micro-Topic 7.5: Example – Evolving a Solution – Goal: Walk through a concrete example of a genetic algorithm solving a problem.

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    • Micro-Topic 7.6: Strengths and Limitations of Genetic Algorithms – Goal: Highlight when to use GAs, and their pros/cons.

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