Evolutionary algorithm facts for kids

Evolutionary algorithms are a special type of computer program. They are inspired by how living things change and adapt over time, which is called evolution. These algorithms are used in Computer science and artificial intelligence to find the best solutions to difficult problems.

Imagine you have a problem, like finding the best way to design a car or schedule flights. Evolutionary algorithms try out many different solutions, like different car designs or flight schedules. They then pick the best ones and combine them to create even better solutions. This process repeats until a really good solution is found.

How Do Evolutionary Algorithms Work?

Evolutionary algorithms follow steps similar to natural evolution. They start with a group of possible solutions. These solutions are like the "individuals" in a population.

Starting with a Population

First, the algorithm creates a starting group of random solutions. This group is called the "initial population." Each solution is a bit different from the others.

Checking How Good Solutions Are (Fitness)

Next, each solution in the population is tested. A "fitness function" measures how good each solution is at solving the problem. Solutions that are better at solving the problem get a higher "fitness score." Think of it like a competition where the best solutions win.

Choosing the Best Solutions

After testing, the algorithm selects the solutions with the highest fitness scores. These are the "fittest" solutions. They are more likely to "survive" and pass on their "traits" to the next generation.

Creating New Solutions (Recombination and Mutation)

The selected solutions then create new solutions for the next "generation." This happens in two main ways:

• Recombination (Crossover): This is like two parent solutions combining their parts to make new "child" solutions. For example, if you're designing a car, parts of one good design might be combined with parts of another good design.
• Mutation: This involves making small, random changes to a solution. It's like a tiny, unexpected change in a living organism. Mutation helps explore new possibilities and prevents the algorithm from getting stuck.

Repeating the Process

The new generation of solutions then goes through the same steps: testing their fitness, selecting the best, and creating even newer solutions. This cycle repeats many times, over many "generations." With each generation, the solutions usually get better and better at solving the problem.

When Do They Stop?

The algorithm keeps going until one of two things happens:

• A solution is found that is good enough.
• A certain number of generations have passed.

Why Are They Useful?

Evolutionary algorithms are very useful for problems that are hard to solve with traditional methods. They can find good solutions even when the problem is very complex or has many possible answers. They are used in many areas, such as:

• Engineering: Designing better airplane wings or car parts.
• Finance: Optimizing investment strategies.
• Computer Science: Training machine learning models or creating robot movements.
• Logistics: Finding the most efficient delivery routes.

They are a powerful tool inspired by nature's own way of finding solutions through evolution.

Images for kids

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  A two-population EA search over a constrained Rosenbrock function with bounded global optimum.

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  A two-population EA search over a constrained Rosenbrock function. Global optimum is not bounded.

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  Estimation of distribution algorithm over Keane's function

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  A two-population EA search of a bounded optima of Simionescu's function.

See also

In Spanish: Algoritmo evolutivo para niños