Genetic algorithms are optimized for known solutions that can be of any type (e.g. integer search, matrix factorization, partitioning, etc.). In contrast, Monte Carlo optimization requires that an optimal solution can be generated by an unknown method. The advantage of genetic algorithms over other optimization methods lies in their automatic control over the number of generations required, initial parameters, evaluation function, and reward for accurate predictions.

An important property of a genetic algorithm is its ability to create a «wild» configuration of parameters (for example, alternating hot and cold endpoints) that correspond to a given learning rate (learning rate times the number of generations). This property allows the user to analyze and decide if the equilibrium configuration is unstable.

The downside of genetic algorithms is their dependence on distributed memory management. While extensive optimization techniques can be used to handle large input sets and multiple processor / core configurations, the complexity of this operation can make genetic algorithm decisions vulnerable to resource constraints that impede progress. Even with the genetic algorithm code, in theory, programs based on genetic algorithms can only find solutions to problems when run on the appropriate computer architecture. Examples of problems associated with a genetic algorithm running on a more limited architecture include memory limits for storing representations of the genetic algorithm, memory limits imposed by the underlying operating system or instruction set, and memory limits imposed by the programmer, such as limits on the amount of processing power, allocated for the genetic algorithm and / or memory requirements.

Many optimization algorithms have been developed that allow genetic algorithms to run efficiently on limited hardware or on a conventional computer, but implementations of genetic algorithms based on these algorithms have been limited due to their high requirements for specialized hardware.

Heterogeneous hardware is capable of delivering genetic algorithms with the speed and flexibility of a conventional computer, while using less energy and computer time. Most implementations of genetic algorithms are based on a genetic architecture approach.

Genetic algorithms can be seen as an example of discrete optimization and computational complexity theory. They provide a short explanation of evolutionary algorithms. Unlike search algorithms, genetic algorithms allow you to control changes in parameters that affect the performance of a solution. For this, the genetic algorithm can study a set of algorithms for finding the optimal solution. When an algorithm converges to an optimal solution, it can choose an algorithm that is faster or more accurate.

In the mathematical language of programmatic analysis, a genetic algorithm is a function that maps states into transitions to the next states. A state can be a single location in a shared space or a collection of states. «Generation» is the number of states and transitions between them that must be performed to achieve the target state. The genetic algorithm uses the transition probability to find the optimal solution, and uses a small number of new mutations each time a generation ends. Thus, most mutations are random (or quasi-random) and therefore can be ignored by the genetic algorithm to test behavior or make decisions. However, if the algorithm can be used to solve the optimization problem, then this fact can be used to implement the mutation step.