Estimation of distribution algorithm
An estimation of distribution algorithm (EDA) is like a game where we try to guess what kind of toys a friend likes based on clues they give us. We start by guessing randomly and writing down all the toys we think they might like. Then we look at the clues they gave us, like "I like toys that are green" or "I don't like toys that make loud noises". We use these clues to figure out which toys are more likely to be their favorites. We take these favorite toys and make new guesses based on them, repeating the process until we are confident we have guessed most of their favorite toys.
In a similar way, an estimation of distribution algorithm is a computer program that tries to find the best solution for a mathematical problem. First, it generates a bunch of random guesses (like guesses for what toys our friend might like). Then it looks at how well each guess solves the problem (like how much our friend likes each toy). It uses this information to create a model of what the best solution might look like (like a list of the most likely favorite toys). Based on this model, it generates new guesses that are more likely to lead to the best solution (like making new guesses based on the list of favorite toys). It keeps doing this until it finds the best solution or runs out of time.
Overall, an EDA is like playing a guessing game with a computer, but instead of guessing about toys, we are guessing about the best solution to a problem. We use clues from our previous guesses to make better guesses until we find the best solution possible.
In a similar way, an estimation of distribution algorithm is a computer program that tries to find the best solution for a mathematical problem. First, it generates a bunch of random guesses (like guesses for what toys our friend might like). Then it looks at how well each guess solves the problem (like how much our friend likes each toy). It uses this information to create a model of what the best solution might look like (like a list of the most likely favorite toys). Based on this model, it generates new guesses that are more likely to lead to the best solution (like making new guesses based on the list of favorite toys). It keeps doing this until it finds the best solution or runs out of time.
Overall, an EDA is like playing a guessing game with a computer, but instead of guessing about toys, we are guessing about the best solution to a problem. We use clues from our previous guesses to make better guesses until we find the best solution possible.