GAs excel at solving optimization problems, aiming to find the best solution among a large set of possibilities. These problems include mathematical function optimization, parameter tuning, portfolio optimization, resource allocation, and more. GAs explore the solution space by enabling the evolution of a population of candidate solutions using genetic operators such as selection, crossover, and mutation, gradually converging towards an optimal or close-to-optimal solution
GAs have applications in machine learning, particularly to optimize the configuration and parameters of machine learning models. GAs can be used to optimize hyperparameters, such as learning rate, regularization parameters, and network architectures in neural networks. They can also be employed for feature selection, where the algorithm evolves a population of feature subsets to identify the most relevant subset for a given task.
Genetic algorithms find application in many designing procedures of mechanical components. For instance, consider the following genetic algorithm example where the aircraft wing design is a kind of designing problem that takes multiple disciplines into consideration. It requires improvement in the ratio of left to drag for a complex wing. The fitness function in genetic optimization is flexible to considerations that come as a demand for a particular design.
A variety of issues can be solved using genetic optimization in the financial market. It helps in finding an optimal combination of parameters that can affect the trades or market rules. You can also find out the near-optimal value for the optimal set of parameters.
Genetic programming in machine learning finds great applications for neural networks in machine learning. We use it for genetic optimization in neural networks or use cases like inheriting qualities of neurons, neural network pipeline optimization, finding the best fit set of parameters for a given neural network, and others.
