Hands-On Genetic Algorithms with Python

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, for example, iteratively moves the current solution in the direction of steepest descent, which is defined by the negative of the given function's gradient.