Genetic Algorithm
Genetic Algorithm is a algorithm in the evolutionary algorithms family. A genetic algorithm searches a large solution space using populations, fitness, selection, crossover and mutation.
Start with this mental model
Genetic Algorithm is a algorithm in the evolutionary algorithms family. A genetic algorithm searches a large solution space using populations, fitness, selection, crossover and mutation.
How it works, without unnecessary jargon
Genetic Algorithm should be understood through its invariant and the state it changes at each step.
The practical complexity in this reading is Varies by implementation / input constraints; always connect that cost to the actual loops, recursion, data structure operations or input assumptions.
Read the algorithm as a sequence of decisions
Encode candidate solutions.
Evaluate fitness.
Select, crossover and mutate repeatedly until a stopping condition.
Read a tiny input by hand and write down the state after every important operation. For Genetic Algorithm, the goal is to make the invariant visible rather than to memorize code.
Varies by implementation / input constraints
Do not quote this complexity mechanically. Ask which operation or input dimension it describes, then derive the cost from the loops, recursion depth, data-structure operations and any preprocessing assumptions.
Good fit
- Useful for hard optimization spaces where exact search is impractical and approximate solutions are acceptable.
What usually goes wrong
- No guarantee of global optimum.
- Poor encoding/fitness can dominate results.
A professional answer shape
A strong interview explanation of Genetic Algorithm should cover the problem it solves, its invariant, the important data structure/state, its Varies by implementation / input constraints cost, and the constraint that would make you choose a different approach.
What problem does Genetic Algorithm solve?
Genetic Algorithm is a algorithm in the evolutionary algorithms family. A genetic algorithm searches a large solution space using populations, fitness, selection, crossover and mutation.
What should I mention about complexity?
Its typical cost here is Varies by implementation / input constraints, but a complete answer should connect that cost to the representation, input constraints and the dominant operations.
What is the most common mistake?
No guarantee of global optimum.