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A function that quantifies the optimality of a solution in a genetic algorithm, often correlating to how 'fit' or 'well-adapted' a solution is within the given environment or problem space.

A class of optimization algorithms inspired by the process of natural selection, where potential solutions to a problem evolve through processes akin to crossover, mutation, and selection.

In evolutionary computation, it refers to a collection of potential solutions evaluated in each iteration of the algorithm, where individuals can be selected for reproduction.

An operator in evolutionary algorithms that introduces random alterations to the genetic representation of a solution to maintain diversity in the population and prevent premature convergence.

A process in genetic algorithms by which individuals are chosen based on their fitness scores to reproduce and populate the next generation, simulating the 'survival of the fittest' principle.

A genetic operator used in genetic algorithms to combine the genetic information of two parents to generate new offspring with pieces of each parent's features.

The basic unit of the genetic representation in evolutionary algorithms, typically corresponding to a component of the solution or a variable in the optimization problem.

The encoded genetic makeup of an individual solution within the context of genetic algorithms, exhibiting the traits or variables that are subjected to evolutionary operations.

In the context of genetic algorithms, a chromosome is a single string of genes, which encodes the solution to the problem that the algorithm is attempting to solve.

The actual expressed traits or the outward, physical manifestation of the genotype in an individual solution in genetic algorithms, seen through the solution's performance or behavior.

A subset of evolutionary computation methods that primarily uses continuous parameter optimization, focusing on the adaptation of strategy parameters such as mutation rate, rather than emphasizing crossover.