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# Genetic Algorithm Assignment Help

Updated: May 11, 2022

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What is a Genetic Algorithm ?

In computer and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of Evolutionary algorithm (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of Genetic Algorithm applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.

Five phases are considered in genetic algorithm

• Initial population

• Fitness function

• Selection

• Crossover

• mutation

Initial population : the process starts with the initial population being generated randomly, allowing the entire range of possible solutions. Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found.

Fiteness function : The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem dependent.

Selection : Selection rules select the individuals, called parents, that contribute to the population at the next generation. The selection is generally stochastic, and can depend on the individuals' scores.

Crossover : Crossover rules combine two parents to form children for the next generation.

Mutation : Mutation rules apply random changes to individual parents to form children.

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