IB Computer Science Topic A4.3.7: Application of Genetic Algorithms Key Definitions

Crossover, also called recombination, is the operator that combines parts of two parent solutions to produce offspring, so features that already score well can be mixed into one candidate.

A fitness function is the rule that scores how good a candidate solution is, and it is the only part of a genetic algorithm that carries what the problem is trying to achieve.

A genetic algorithm is an optimisation technique that keeps a population of candidate solutions and improves it over successive generations using selection, crossover and mutation.

Mutation is the operator in a genetic algorithm that randomly alters part of a candidate solution, which keeps variety in the population and lets the search reach features no parent held.

Premature convergence is the state where every candidate in the population has become near-identical, so crossover returns copies and the search stalls at a local optimum.