Genetic algorithms have a variety of applications, and one of the basic applications of genetic algorithms can be the optimization of problems and solutions. We use optimization for finding the best solution to any problem. Optimization using genetic algorithms can be considered genetic optimization, and there are several benefits of performing optimization using genetic algorithms. In this article, we are going to list down 10 real-life applications of genetic optimization.
Let’s start with these interesting applications one-by-one.
1. Traveling salesman problem (TSP)
This is one of the most common combinatorial optimization problems in real life that can be solved using genetic optimization. The main motive of this problem is to find an optimal way to be covered by the salesman, in a given map with the routes and distance between two points. If genetic algorithms are used in finding the best route structure, we don't get the solution only once. After each iteration, we can generate offspring solutions that can inherit the qualities of parent solutions. TSP has a variety of applications like planning, logistics, and manufacturing.
2. Vehicle routing problem (VRP)
The basic vehicle routing problem (VRP) can be considered as a generalization of the TSP problem which is also a combinatorial optimization problem. In this problem we find an optimal weight of goods to be delivered or find an optimal set of delivery routes when other things like distance, weights, depot points are constrained or have any kind of restrictions. Genetic approaches are competitive with tabu search and simulated annealing algorithms in terms of solution time and quality.
3. Financial markets
In the financial market, using genetic optimization, we can solve a variety of issues because genetic optimization helps in finding an optimal set or combination of parameters that can affect the market rules and trades. For example, in the stock market, any rule is a popular tool for analysis, research, and deciding to buy or sell shares. In this example, the success of trading depends on the selection of optimal values for all parameters and combinations of parameters. Genetic algorithms can help in finding the optimal and sub-optimal combinations of parameters. Also by genetic optimization, we can find out the near-optimal value from the set of combinations.
4. Manufacturing system
One of the major applications of genetic optimization is to minimize a cost function using the optimized set of parameters. In manufacturing we can see various examples of cost function and finding an optimal set of parameters for this function can be performed by following the genetic optimization. In many cases, we can find the application of genetic optimization in product manufacturing (variation of production parameters or comparison of equipment layout). The main motive behind applying genetic optimization is to achieve an optimum production plan by taking into consideration dynamic conditions like inventories, capacity, or material quality.
5. Mechanical engineering design
In many designing procedures of mechanical components, we can also find the application of genetic optimization. We can take aircraft wing design as an example where we are required to improve the ratio of lift to drag for a complex wing. This kind of designing problem can be considered as a multidisciplinary problem, the fitness function in genetic optimization can be altered by considering some specific requirement of the design.
6. Data clustering and mining
Data clustering can be considered an unsupervised learning process where we try to segment data based on the characteristic of data points. One of the major parts of the procedure is to find out the centre point of the clusters and we know that genetic algorithms have great capability of searching for an optimal value. In data clustering and mining we can use genetic algorithms to find a data centre with an optimal error rate.
7. Image processing
There are various works and researches which show the use cases of genetic optimization in various image processing tasks. One of the major tasks related to genetic approach in image processing is image segmentation. Although these genetic optimizations can be utilized in various areas of image analysis to solve complex optimization problems. Using genetic optimization in an integrated manner with image segmentation techniques can make the whole procedure an optimization problem.
8. Neural networks
Neural networks in machine learning are one of the biggest areas where genetic algorithms have been used for optimization. One of the simplest examples of use cases of genetic optimization in neural networks is finding the best fit set of parameters for a neural network. Instead of these, we can find the use of genetic algorithms in neural network pipeline optimization, inheriting qualities of neurons, etc.
9. Wireless sensor networks
The wireless sensor network is a network that includes spatially dispersed and dedicated centres to maintain the records about the physical conditions of the environment and pass the record to a central storage system. Some notable parameters are the lifetime of the network and energy consumption for routing which plays key roles in every application. Using the genetic algorithms in WSN we can simulate the sensors and also a fitness function from GA can be used to optimize, and customize all the operational stages of WSNs.
10. Medical science
In medical science, we can find many examples of use cases of genetic optimization. The generation of a drug to diagnose any disease in the body can have the application of genetic algorithms. In various examples, we find the use of genetic optimization in predictive analysis like RNA structure prediction, operon prediction, and protein prediction, etc. also there are some use cases of genetic optimization in process alignment such as Bioinformatics Multiple Sequence Alignment, Gene expression profiling analysis, Protein folding, etc.
So these are the 10 real-life interesting applications where genetic optimization is used widely. These algorithms are part of the evolutionary algorithm family that is based on the principles of natural evaluation explained in Charles Darwin's theory of evolution.
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