Limitations of genetic algorithms
To get the most out of genetic algorithms, we need to be aware of their limitations and potential pitfalls.
The limitations of genetic algorithms are as follows:
- The need for special definitions
- The need for hyperparameter tuning
- Computationally intensive operations
- The risk of premature convergence
- No guaranteed solution
We will cover each of these in the upcoming sections.
Special definitions
When applying genetic algorithms to a given problem, we need to create a suitable representation for them – define the fitness function and the chromosome structure, as well as the selection, crossover, and mutation operators that will work for this problem. This can often prove to be challenging and time-consuming.
Luckily, genetic algorithms have already been applied to countless different types of problems, and many of these definitions have been standardized. This book covers numerous types of real-life problems...
