# Optimization Evolutionary Algorithms

Learn about the evolutionary algorithms and their usage in optimization.

## What are evolutionary algorithms?

**Evolutionary algorithms** are a family of optimization algorithms inspired by biological evolution. They are used to solve optimization problems in which the goal is to find the best solution among a large number of possible solutions. Evolutionary algorithms are based on the principles of natural selection, reproduction, and mutation.

Note:The basic idea of an evolutionary algorithm is to create a population of potential solutions to a problem and then use natural selection, mutation, and recombination to create new populations of potentially better solutions. The process continues until a satisfactory solution is found or a predetermined stopping criterion is met.

The steps involved in an evolutionary algorithm typically include initialization, evaluation, selection, recombination, mutation, and termination. During initialization, a population of solutions is randomly generated. Then, the solutions are evaluated based on a fitness function that measures their quality. In the selection step, the fittest solutions are selected for recombination, which creates new solutions by combining parts of the selected solutions. The mutation step introduces random changes to the new solutions. The process continues until a satisfactory solution is found, or a predetermined stopping criterion is met.

## How does the evolutionary algorithm work?

Let’s start with a high-level explanation of the evolutionary algorithm:

**Initialization:**We generate an initial population of candidate solutions randomly.**Evaluation:**We evaluate each candidate solution using a fitness function that quantifies how well it solves the problem.**Selection:**We select a subset of the candidate solutions to be parents of the next generation based on their fitness.**Recombination:**We create new candidate solutions by combining parts of the parent solutions.**Mutation:**We introduce random changes to the new candidate solutions to increase diversity.**Replacement:**We replace the old population with the new population.**Termination:**We repeat steps 2-6 until a satisfactory solution is found or a stopping criterion is met.

Let’s look at the algorithm in more detail in the following illustration:

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