When faced with complex problems that do not have a straightforward solution, it can be beneficial to explore alternative approaches. One such approach is using genetic algorithms (GAs) to find intelligent solutions that surpass human capabilities. In this article, we will discuss the concept of genetic algorithms and how they can be implemented in SQL Server.
A genetic algorithm is a search heuristic inspired by the process of natural selection. It involves creating a population of potential solutions and iteratively evolving them to find the best solution. GAs are particularly useful when the number of possible permutations is too large to perform a full search.
Let’s consider an example problem to better understand how genetic algorithms work. Imagine a 10×10 grid surrounded by walls, with half of the grid squares containing empty soda cans. A robot starts in the upper left square and executes 200 actions, such as moving in different directions or picking up a can. The goal is to maximize the robot’s score by picking up cans without penalties.
To implement a genetic algorithm solution in SQL Server, we follow these steps:
- Generate an initial population of children.
- Test each child in a simulator to evaluate its performance.
- Select the best-performing children and eliminate the rest.
- Cross-breed the selected children to create a new generation of children.
- Introduce random mutations into each child to promote diversity.
- Repeat the process of selection, replication, and mutation until a satisfactory solution is found.
In our example, each child represents a possible sequence of actions for the robot. The robot’s DNA consists of 243 genes, each representing one of six possible actions (move in a direction, pick up a can, or move randomly). The number of unique children that can be generated is astronomical, making a brute force approach impractical.
By running simulations on a subset of evolved children, we can obtain an almost ideal solution. The included SQL scripts provide the necessary stored procedures to score the performance of each child, create and evolve robots, and decipher the rules they come up with.
It is fascinating to observe how the evolved robots develop heuristics that a human might not consider. For example, they may prioritize picking up cans from the beginning of a string rather than from the middle, making it easier to backtrack to stranded cans. The intelligence of the robots is solely derived from the evolutionary process.
Compared to hand-coding and optimizing the genes, the genetic algorithm approach yields significantly better results in a fraction of the time. While humans may score around 350 points after days of optimization, the GA can achieve higher scores in just 20 minutes of evolution.
As an experiment, you can try implementing variations of the problem, such as randomly populated grids, allowing diagonal movements, or adjusting the generations and offspring counts. This can lead to the emergence of interesting heuristics. Feel free to share your findings and any strategies to speed up the evolution process.
Genetic algorithms offer a powerful tool for solving complex problems in SQL Server. By leveraging the principles of natural selection, we can find intelligent solutions that surpass human capabilities. The ability to evolve and adapt makes genetic algorithms a valuable addition to any developer’s toolkit.