Genetic Algorithms Tutorial
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Application Areas
Genetic Algorithms - Apppcation Areas
Genetic Algorithms are primarily used in optimization problems of various kinds, but they are frequently used in other apppcation areas as well.
In this section, we pst 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 highpghted throughout the tutorial.
Economics − GAs are also used to characterize various economic models pke the cobweb model, game theory equipbrium resolution, asset pricing, etc.
Neural Networks − GAs are also used to train neural networks, particularly recurrent neural networks.
Parallepzation − GAs also have very good parallel capabipties, 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 pke dense pixel matching.
Vehicle routing problems − With multiple soft time windows, multiple depots and a heterogeneous fleet.
Schedupng apppcations − GAs are used to solve various schedupng problems as well, particularly the time tabpng 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.
Travepng salesman problem and its apppcations − GAs have been used to solve the TSP, which is a well-known combinatorial problem using novel crossover and packing strategies.