Role of Exploration and Exploitation in Genetic Algorithms
Explore how genetic algorithms use exploration and exploitation to find optimal solutions. Understand the difference between informed and brute-force searches and how crossover and mutation help navigate search spaces efficiently.
Informed search vs. brute-force search
In the last lesson, we introduced brute-force search and “informed search, the two basic approaches used to solve search problems. Let’s take a look in more detail and what differentiates them from one another.
An informed search is basically an algorithm that relies on a search strategy where we make smart decisions based on the available information. By contrast, in a brute-force search, we iterate over every possible solution linearly, and therefore, these searches use no knowledge of the search area to make decisions.
For example, in a maze, a brute-force solution would be to try every possible path, never stopping to consider whether or not the paths are becoming smaller or larger, or if the paths will even lead to an exit. Brute-force searches are naive. Eventually, we will find a solution, but it might take a long time, and it might not even be the best one.
An informed search, on the other hand, will keep track of the paths which have already been visited. Then using this knowledge, it ...