Differences from traditional algorithms
There are several important differences between genetic algorithms and traditional search and optimization algorithms, such as gradient-based algorithms.
The key distinguishing factors are as follows:
- Maintaining a population of solutions
- Using a genetic representation of the solutions
- Utilizing the outcome of a fitness function
- Exhibiting a probabilistic behavior
We will describe these factors in greater detail in the following sections.
Population-based
The genetic search is conducted over a population of candidate solutions (individuals) rather than a single candidate. At any point in the search, the algorithm retains a set of individuals that form the current generation. Each iteration of the genetic algorithm creates the next generation of individuals.
In contrast, most other search algorithms maintain a single solution and iteratively modify it in search of the best solution. The gradient descent algorithm...
