Q25, Q20, Q21, Q27 — learn the vocabulary before operators
Green = exact question bank answerAmber = clarification to help you understand
Why start here?
Every other exam topic uses these four words: population, chromosome, gene, and fitness function. Master them first and the rest of the course clicks into place.
Analogy: Think of a GA like breeding dogs. The population is your kennel. Each chromosome is one dog's full DNA profile. Each gene is one trait (ear size, coat color). The fitness function is the dog show judge — it scores how good each dog is.
Exam questions
Q25
Explain some basic concepts and terms related to Genetic Algorithm?
📋 Question bank answer
Population: This is a subset of all the probable solutions that can solve the given problem. Chromosomes: A chromosome is one of the solutions in the population. Gene: This is an element in a chromosome. Fitness function: This is a function that uses a specific input to produce an improved output.
💡 Clarification
The diagram below shows how these nest: population → chromosomes → genes. Better fitness = more likely to survive and reproduce.
Visual aid
How the terms nest inside each other
POPULATION
Chromosome 1 one solution
1
gene
0
1
1
0
fitness0.82
Chromosome 2
0
1
0
0
1
fitness0.45
Chromosome 3 ★ best
1
1
0
1
1
fitness0.91
…and 97 more chromosomes (100 total in this population)
Population
The whole group of solutions
Chromosome
One complete solution inside it
Gene
One building block (here: 0 or 1)
Exam questions
Q20
What is a genotype in Genetic Algorithms?
📋 Question bank answer
The genotype is the encoded representation of a solution, typically in the form of a binary string, array, or other structured format. It's the internal format used by genetic operators like crossover and mutation during evolution.
💡 Clarification
Genotype = internal code the GA edits. Memory hook: genotype = genetics inside the machine.
Q21
What is a phenotype in Genetic Algorithms?
📋 Question bank answer
The phenotype is the actual solution as interpreted from the genotype. It is the version of the solution evaluated by the fitness function and used in the real-world problem domain.
💡 Clarification
Phenotype = decoded real-world solution. Memory hook: phenotype = physical/form in the problem.
Visual aid
Genotype → Phenotype → Fitness (knapsack example)
Genotype
1 0 1 1
Internal code the GA mutates & crosses over
→
decode
→
Phenotype
✓ Item A
✗ Item B
✓ Item C
✓ Item D
Real packing plan
→
Fitness
$840
Score from fitness function
Each 1 = take the item · Each 0 = skip it · The judge only sees the phenotype, not the raw bits
Q27
How does the fitness function affect a GA's performance?
📋 Question bank answer
The fitness function evaluates how good each solution is. A well-designed fitness function guides the algorithm toward better solutions, while a poor one can lead to misleading or suboptimal results.
💡 Clarification
The fitness function is the compass of the GA — if it rewards the wrong thing, the algorithm confidently evolves toward bad answers no matter how good your operators are.
Practice — write the exam answers
Read the question, write your answer, then reveal the bank answer to check.
Q25
Explain some basic concepts and terms related to Genetic Algorithm?
Practice 1 of 4 — write your answer below
Q20
What is a genotype in Genetic Algorithms?
Practice 2 of 4
Q21
What is a phenotype in Genetic Algorithms?
Practice 3 of 4
Q27
How does the fitness function affect a GA's performance?