# Genetic Algorithm in Machine Learning

A genetic algorithm is an adaptable heuristic search method inspired by "Darwin's theory of evolution in Nature," It is used in machine learning to tackle optimization issues. It is a subset of evolutionary algorithms employed in computer science. It solves optimization issues by combining genetic and natural selection techniques.

Genetic algorithms are essential in solving difficult challenges that would have taken longer to overcome. Genetic algorithms are frequently employed in a variety of real-world applications, such as engineering electronic circuits, cracking codes, image processing, and artificial creativity.

## How Genetic Algorithms Work

To develop high-quality solutions, the genetic algorithm employs the evolutionary generational cycle. These algorithms employ several processes that either improve or modify the demographic to provide a better-fit answer.

Tackling difficult optimization issues comprises five steps, which are:

Initialization- A genetic algorithm's procedure begins with the generation of a population of people. Each person is the solution to the given situation. Genes are a set of factors that define or define an individual. The solution to the problem is to assemble genes into a string and make chromosomes. The usage of random binary strings is one of the most prevalent initialization strategies.

Fitness Assignment- Fitness refers to a person's capacity to compete with other people. Individuals are assessed based on their fitness function in each iteration. This score impacts the likelihood of being chosen for reproduction. The higher the fitness score, the more likely it is that the individual will be picked for reproduction.

Selection- The selection phase entails the selection of individuals for progeny reproduction. To enhance reproduction, all the chosen individuals are put in pairs of two. These individuals then pass on their genes to the following generation. There are 3 modes of selection used;

• Roulette wheel selection
• Tournament selection
• Rank-based selection

Reproduction- The reproduction stage results in the birth of a kid. The genetic algorithm employs two variation operators in this stage, which are administered to the parent population.

• Crossover- A crossover point within the genes is chosen at random throughout this procedure. The crossover operator then switches the genetic information of two current generation parents to create a new person representing the child.
• Mutation-To preserve population variety, the mutation operator introduces random genes into the progeny. It is possible to accomplish this by flipping certain chromosomal bits. Mutation aids in the resolution of the issue of early convergence and increases diversification.

Termination- Following the reproduction phase, a stopping criterion is used as the foundation for termination. When the threshold fitness solution is attained, the algorithm finishes. It will select the final answer as the best in the population.

• Genetic algorithms have the most parallel capabilities.
• It aids in the optimization of a variety of issues, including discrete functions, multi-objective issues, and continuous functions.
• It offers a remedy to a problem that becomes better over time.
• A genetic algorithm does not require derivative data

## Limitations of Genetic Algorithms

• Genetic algorithms are inefficient at tackling basic problems.
• It does not ensure the final quality of a problem solution.
• Calculating fitness values repeatedly may provide some computing difficulties.

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