Genetic algorithms (GA’s) are computer algorithms that are analogous to human genetics. Genetic Algorithms have wide varieties of applications spread over the field of computing. These algorithms have been prevalent in many sub-fields of Data Science like Machine Learning, NLP, and Data Mining etc. Genetic Algorithm finds out the best outputs by mimicking human evolution. Genetic Algorithms provide a great heuristic approach to solve complex combinatorial problems. GA’s are also used to find optimization results for a large solution space.
The algorithm begins with a set of solutions which are analogous to chromosomes related to a population of a city. This population will contain people with good or bad genetic characteristics where the fittest person is chosen for further continuity of population. Genetic Algorithm is focused on the thought that the new population is always better as a result of evolution. Genetic Algorithms works in 4 brief steps:
1. Selection. Selecting the finest individuals with the best genes
2. Crossover. Offspring is created by exchanging the parent genes.
3. Mutation. Convergence of offspring takes place in this step.
4. Termination. It marks the end of the process.
Important Applications of Genetic Algorithm in Data Science
1. Machine Learning and Deep Learning
Deep Learning is a vast field and GAs are used to concur many deep learning algorithms. It is a highly considered alternative for reinforcement learning. DNN’s when combined with the efforts of Genetic Algorithms makes up for great efficiency and better results. Genetic Algorithms are highly used for the purposes of feature selection in machine learning. Feature Selection requires heuristic processes to find an optimal machine learning subset which is made possible with the help of a Genetic Algorithm. The primary requirement of Machine Learning is to get some initial data. Sometimes this initial data is very large. When this data is large, a genetic algorithm is used to significantly reduce the required information for its efficient use.
2. Robotics & Artificial Intelligence
Robotics helps to ease the human effort and gets the best results in the field of Data Science. It is a combination of complex mathematics and overwhelming algorithms, genetic is one of them. GA’s are highly used in a range of tasks like finding the optimal designs and components. GA’s also helped in the development of multipurpose robots.
3. Image Processing
Image processing is a highly required preemptive technique in data science. Image processing uses GAs to understand and accomplish the tasks of segregation as a prior step in AI development. It is highly used in image segmentation and enhancement. Genetic Algorithms are adopted for faster processing time and also to enhance the images by solving complex optimization problems.
4. Artificial Neural Networks (ANN)
ANN’s are the basis of deep learning and the GAs have been used as a factor in the evolution of ANNs. The Genetic Algorithm techniques are used to optimize the ANNs for the purpose of Image Processing. As Genetic Algorithms are mostly related to Machine Learning, Genetic Algorithms are highly involved in finding the better subsets of data for its utilization in further steps of training an ANN.
5. Natural Language Processing
NLP is a crucial component in Artificial Intelligence. NLP is an in-demand area of data science that uses Genetic Algorithms in its many sub-areas. It focuses on the ability of the machines to communicate like humans do by learning how to speak as humans do. GA’s are mostly associated with feature extraction and data handling in NLP. Also, the Genetic Algorithm is used to find relationships in language generation to solve a given NLP problem.
Final Words About Genetic Algorithm
The Genetic Algorithm comes into picture when we are required in regard to finding an optimal solution of a given problem. The applications of genetic algorithms range from economics to Bioinformatics but are excessively used in Data Science due to the applications we can carry out using GA’s in these areas.
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