Module 2 of 6
GA Operators & Strategies
Q1, Q26, Q14, Q2, Q3, Q15, Q16, Q24
Green = exact question bank answer
Amber = clarification only
Exam questions & visual aids
Crossover — recombining parents
Crossover takes two parent chromosomes and produces offspring by swapping genetic material.
📋 Question bank answer
One-point crossover: A single crossover point is chosen, and the segments after that point are swapped between the parents.
Two-point crossover: Two crossover points are selected, and the segment between them is exchanged.
📋 Question bank answer
In Single-point Crossover, a location is selected randomly on both the parents. The location is the same in both the parents chromosome. The genes to the right of the crossover point is swapped between the two parents.
In Two-point Crossover, two locations are selected randomly in the parents' chromosome. Then the genes in between the two points are swapped between the two parents.
💡 Clarification
See the diagrams below — one-point swaps the right segment; two-point swaps only the middle.
One-point crossover — swap everything to the right of the cut
↓ swap right segments ↓
from Parent A
from Parent B
cut point (same on both)
Two-point crossover — swap only the middle segment
↓ swap middle only ↓
swapped middle
Ends stay with original parent
Mutation — introducing novelty
Mutation types (Q14) — pick based on chromosome format
Bit-flip Binary strings
one bit flips 1→0 or 0→1
Scramble Shuffle a section
Swap Permutation problems
two positions exchange
Gaussian Real-valued numbers
2.4 + noise → 2.7
add small random value
📋 Question bank answer
Types include bit-flip (for binary), Scramble Mutation, swap (for permutation), and Gaussian mutation (for real-valued). The choice depends on the chromosome representation.
💡 Clarification
The mutation cards above show what each type looks like in practice.
📋 Question bank answer
When diversity in the population is low or the search is stuck in local optima, mutation helps by introducing new genetic material, whereas crossover relies on existing traits and may not help in exploration.
Selection & preservation
Tournament selection (Q2) — pick the best from a random group
Random group of 4 → highest fitness (★) wins and reproduces
Elitism (Q15)
Gen N
★ best
→
Gen N+1
★ same
Best pass unchanged — no crossover or mutation on them
Immigration (Q24)
Move good individuals between islands — spreads diversity
Opposition-based learning (Q16)
Solution
0.7
vs
Opposite
0.3
→
Pick better
direction
Evaluate both a point and its mirror — escape local optima
📋 Question bank answer
It randomly selects a group of individuals and chooses the best among them for reproduction. It's simple and effective for maintaining diversity and avoiding premature convergence.
📋 Question bank answer
Elitism ensures the best individuals from the current generation are carried to the next without modification, helping preserve high-quality solutions.
📋 Question bank answer
It's a method where both a candidate solution and its opposite are evaluated. It helps escape local optima by considering alternative search directions.
📋 Question bank answer
Immigration refers to moving individuals between separate sub-populations during evolution. It allows high-quality solutions to spread, increases genetic diversity, and helps avoid premature convergence by introducing new genetic material into each sub-population.
💡 Clarification
The diagram above shows good individuals moving between sub-populations (islands).
Quick-fire quiz
Q1: In two-point crossover, what gets exchanged?
A) Genes after a single cut point
B) Genes between two cut points
C) Random individual genes scattered throughout
Q3: When should you prefer mutation over crossover?
A) When the population is highly diverse
B) When you want to recombine good traits
C) When diversity is low or stuck in local optima
Q14: Which mutation type is used for real-valued chromosomes?
A) Gaussian mutation
B) Bit-flip mutation
C) Swap mutation