Genetic Algorithms aim to find the best possible solution to a problem by mimicking the process of natural selection and evolution.

The initial set of solutions in a Genetic Algorithm is referred to as the Population.

The process of choosing solutions from the population based on their fitness is known as Selection.

The process of combining genetic material from two parent solutions to create a new solution is called Crossover.

The process of introducing random changes to the genetic material of a solution is called Mutation.

The process of evaluating the fitness of each solution in the population is called Evaluation.

The term used to describe the measure of how well a solution performs in solving the problem is Fitness.

The term used to describe the set of all possible solutions to a problem is Search Space.

The term used to describe the process of stopping the Genetic Algorithm when a satisfactory solution is found is Convergence.

The term used to describe the process of representing a solution as a string of symbols is Encoding.

The term used to describe the process of converting a string of symbols back into a solution is Decoding.

The term used to describe the process of selecting the best solutions from the population and using them to create the next generation is Elitism.

The term used to describe the process of randomly selecting solutions from the population and using them to create the next generation is Selection.

The term used to describe the process of combining genetic material from multiple parent solutions to create a new solution is Crossover.

The term used to describe the process of introducing random changes to the genetic material of a solution is Mutation.