[amp_mcq option1=”Fitness function & Crossover techniques” option2=”Crossover techniques & Random mutation” option3=”Individuals among the population & Random mutation” option4=”Random mutation & Fitness function” correct=”option1″]
The correct answer is: A. Fitness function & Crossover techniques
A genetic algorithm is a search heuristic that is inspired by the process of natural selection. It is used to find optimal solutions to problems that are difficult to solve using traditional methods.
The two main features of a genetic algorithm are:
- Fitness function: This is a function that evaluates the quality of a solution. The fitness function is used to select the best solutions from the population.
- Crossover techniques: These are techniques that are used to create new solutions from existing solutions. The crossover techniques are used to generate a new population of solutions.
The other options are incorrect because they are not essential features of a genetic algorithm.
- Individuals among the population: The individuals in the population are the solutions that are being evaluated by the fitness function. The individuals are not essential for the genetic algorithm to work.
- Random mutation: Random mutation is a technique that is used to introduce new genetic material into the population. Random mutation is not essential for the genetic algorithm to work.