Before you read this article: Have you read my previous article on search algorithms?
Search Algorithms In a Nutshell
What could be better than funny analogies to help us understand a concept?
In the world of complex searching algorithms, we often find ourselves navigating between two distinct ends of the spectrum: simplicity and complexity.
Most notably, (extremely) simple algorithms can be characterized as below:
- Breadth-first: exploring each level of nodes one by one
- Depth-first: exploiting a solution as long as possible
The problem is that both simple approaches operate in a rigid manner, without having any adjustable parameters.
Now, consider the converse — a sophisticated algorithm that employs a finely tuned blend of intelligent heuristics and hyperparameters. Below is the list of required arguments for Simulated Annealing:
“In order to apply the simulated annealing method to a specific problem, one must specify the following parameters: the state space, the energy (goal) function
E(), the candidate generator procedure
neighbour(), the acceptance probability function
P(), and the annealing schedule
temperature()AND initial temperature
The unique strength of this approach lies in its adaptability. By fine-tuning the individual arguments assigned to each heuristic, such complex algorithms can be tailored to address specific scenarios effectively.
However, this adaptability comes at a cost — a lack of generalization. The algorithm’s fine-tuning may perform exceptionally well in one context but struggle in another.
This trade-off between adaptability and generalization presents its own set of challenges and potential problems. Explainability, for example, may be an issue: can you explain “why the energy function is chosen logarithmic” or “how the temperature relates to the…