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Short note: Genetic algorithm

Short answer

A genetic algorithm is a search and optimisation method inspired by natural selection. A population of encoded solutions evolves through fitness evaluation, selection, crossover and mutation until a stopping condition. It searches many points at once and avoids local optima, but can be slow and parameter-dependent.

Write a short note on Genetic Algorithm.

A Genetic Algorithm (GA) is a search and optimisation technique based on natural selection and genetics ("survival of the fittest"). In data mining it is used for classification-rule discovery, feature selection, clustering and tuning of neural networks.

Basic idea

A population of candidate solutions evolves over generations. Each solution is encoded as a chromosome, often a bit string (e.g. the rule IF A1 AND NOT A2 THEN C2 is encoded as 1 0 0).

Steps

  1. Create an initial population of random chromosomes.
  2. Compute the fitness of each (e.g. classification accuracy of the rule).
  3. Selection: choose fitter chromosomes as parents.
  4. Crossover: exchange parts of two parents to make offspring.
  5. Mutation: randomly flip some bits with small probability to keep diversity.
  6. Form the new population; repeat from step 2 until a stopping condition (required fitness or maximum generations) is met.
Population -> Fitness -> Selection -> Crossover
     ^                                  |
     +------- Mutation <----------------+

Features

It searches many points at once, so it is less likely to get stuck in local optima, and it needs no gradient. However, it can be slow, and the result depends on the encoding and the parameters.

✓ Answer checked by Claude Opus 5.5 (high effort)

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