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Crossover and mutation in GA

Short answer

Crossover combines two parent chromosomes with high probability (e.g., 0.6–0.9) to produce offspring, exploiting good building blocks. Mutation randomly flips bits with very low probability (e.g., 0.001–0.01) to maintain diversity and escape local optima.

Write a short note on crossover and mutation in Genetic Algorithm.

In a Genetic Algorithm a new generation is produced from the current population using the genetic operators crossover and mutation, after the fitter individuals have been selected as parents. Each solution is encoded as a chromosome, usually a bit string.

Crossover (recombination)

Crossover combines the genetic material of two parent chromosomes to produce one or two children that inherit features of both. It is applied with a high probability (typically 0.6 to 0.9) and exploits the good parts of existing solutions.

  • Single-point crossover: choose a cut point and swap the tails.
  • Two-point crossover: swap the segment between two cut points.
  • Uniform crossover: each bit is taken from either parent at random.
Parent 1: 1 1 0 0 | 1 0 1      Child 1: 1 1 0 0 | 0 1 0
Parent 2: 0 1 1 1 | 0 1 0      Child 2: 0 1 1 1 | 1 0 1
               cut point

Mutation

Mutation randomly changes one or a few bits of a chromosome (0 becomes 1 and the reverse), with a very small probability (e.g. 0.001 to 0.01 per bit). It restores lost genetic material, maintains diversity in the population and helps the search escape local optima.

Before: 1 1 0 0 1 0 1
After : 1 1 0 1 1 0 1     (4th bit flipped)
BasisCrossoverMutation
ParentsTwoOne
ProbabilityHighVery low
RoleMixes good building blocks (exploitation)Adds new variation (exploration)

Too high a mutation rate makes the search random; too low a rate gives premature convergence.

✓ Answer checked by Claude Opus 5.5 (high effort)

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