Genetic Algorithms are primarily used in optimization problems of various kinds, but they are frequently used in other application areas as well.
In this section, we list some of the areas in which Genetic Algorithms are frequently used. These are −
Optimization − Genetic Algorithms are most commonly used in optimization problems wherein we have to maximize or minimize a given objective function value under a given set of constraints. The approach to solve Optimization problems has been highlighted throughout the tutorial.
Economics − GAs are also used to characterize various economic models like the cobweb model, game theory equilibrium resolution, asset pricing, etc.
Neural Networks − GAs are also used to train neural networks, particularly recurrent neural networks.
Parallelization − GAs also have very good parallel capabilities, and prove to be very effective means in solving certain problems, and also provide a good area for research.
Image Processing − GAs are used for various digital image processing (DIP) tasks as well like dense pixel matching.
Vehicle routing problems − With multiple soft time windows, multiple depots and a heterogeneous fleet.
Scheduling applications − GAs are used to solve various scheduling problems as well, particularly the time tabling problem.
Machine Learning − as already discussed, genetics based machine learning (GBML) is a niche area in machine learning.
Robot Trajectory Generation − GAs have been used to plan the path which a robot arm takes by moving from one point to another.
Parametric Design of Aircraft − GAs have been used to design aircrafts by varying the parameters and evolving better solutions.
DNA Analysis − GAs have been used to determine the structure of DNA using spectrometric data about the sample.
Multimodal Optimization − GAs are obviously very good approaches for multimodal optimization in which we have to find multiple optimum solutions.
Traveling salesman problem and its applications − GAs have been used to solve the TSP, which is a well-known combinatorial problem using novel crossover and packing strategies.