There are several important differences between genetic algorithms and traditional search and optimization algorithms, such as gradient-based algorithms.

The key characteristics of genetic algorithms distinguishing them from traditional algorithms are:

• Maintaining a population of solutions
• Using a genetic representation of the solutions
• Utilizing the outcome of a fitness function
• Exhibiting a probabilistic behavior

In the upcoming sections, we will describe these factors in greater detail.

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.

Instead of operating directly on candidate solutions, genetic algorithms operate on their representations (or coding), often referred to as chromosomes. An example of a simple chromosome is a fixed-length binary string.

The chromosomes allow us to facilitate the genetic operations of crossover and mutation. Crossover is implemented by interchanging chromosome parts between two parents, while mutation is implemented by modifying parts of the chromosome.

A side effect of the use of genetic representation is decoupling the search from the original problem domain. Genetic algorithms are not aware of what the chromosomes represent and do not attempt to interpret them.

The fitness function represents the problem we would like to solve. The objective of genetic algorithms is to find the individuals that yield the highest score when this function is calculated for them.

Unlike many of the traditional search algorithms, genetic algorithms only consider the value that's obtained by the fitness function and do not rely on derivatives or any other information. This makes them suitable to handle functions that are hard or impossible to mathematically differentiate.

While many of the traditional algorithms are deterministic in nature, the rules that are used by genetic algorithms to advance from one generation to the next are probabilistic.

For example, when selecting the individuals that will be used to create the next generation, the probability of selecting a given individual increases with the individual's fitness, but there is still a random element in making that choice. Individuals with low score values can still be chosen as well, although with a lower probability.

Mutation is probability-driven as well, usually occurs with low likelihood, and makes changes at random location(s) in the chromosome.

The crossover operator can have a probabilistic element as well. In some variations of genetic algorithms, the crossover will only occur at a certain probability. If no crossover takes place, both parents are duplicated into the next generation without change.

Despite the probabilistic nature of this process, the genetic algorithm-based search is not random; instead, it uses the random aspect to direct the search toward areas in the search space where there is a better chance to improve the results. Now, let's look at the advantages of genetic algorithms.