Contents
What is genetic algorithm in Soft Computing?
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve.
What are the advantages of Algorithm?
Advantages of Algorithms:
- It is a step-wise representation of a solution to a given problem, which makes it easy to understand.
- An algorithm uses a definite procedure.
- It is not dependent on any programming language, so it is easy to understand for anyone even without programming knowledge.
How is a genetic algorithm used for feature selection?
Genetic Algorithm for Feature Selection To implement the Genetic Algorithm for Feature Selection, the accuracy of the predictive model is considered as the fitness of the solution, where the accuracy of the model is obtained by using the Logistic Regression.
How to implement a genetic algorithm in Python?
The python code for basic Genetic Algorithm operators is provided below. To implement the Genetic Algorithm for Feature Selection, the accuracy of the predictive model is considered as the fitness of the solution, where the accuracy of the model is obtained by using the Logistic Regression.
How is feature selection used in data wrangling?
Feature Selection is the process in Data Wrangling, where certain features that contribute most to the Target Variable are selected. Learning from irrelevant features in the data can decrease the Accuracy and Performance of the model.
Which is the best way to select a feature set?
In this direction, feature selection plays a crucial role. Different techniques are present such as forwards selection, backward elimination, stepwise selection, etc. to select a feature set. However, most of these approaches are performed manually and are computationally expensive and time-consuming.