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Genetic Algorithm and Machine Learning

Genetic Algorithm and Machine Learning

Radha Raman Chandan (School of Management Sciences, India), Sarita Soni (Government Polytechnic, Bighapur, India), Atul Raj (Government Polytechnic, Sikandra, India), Vivek Veeraiah (Adichunchanagiri University, India), Dharmesh Dhabliya (Vishwakarma Institute of Information Technology, India), Sabyasachi Pramanik (Haldia Institute of Technology, India), and Ankur Gupta (Vaish College of Engineering, India)
Copyright: © 2023 |Pages: 16
DOI: 10.4018/978-1-6684-5656-9.ch009
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Abstract

Genetic algorithm is based on the natural search process, which mimics natural growth and employs approaches inspired by natural evolution to solve optimization problems, employing bequest, mutation, and miscellany, as well as intersect. Its actual meaning is a competent, concurrent, and universal search approach that continuously obtains and builds up knowledge about search space and command management search space in order to alter the best search result. The traditional multilevel association rules mining techniques generate a large number of candidate items and compare them to the whole database. Nonetheless, the majority of mining procedures are in vain, since they guide crucial costs associated with computing. The inherited algorithms provide a novel technique for tackling these sorts of problems.
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Introduction

John Holland created the Genetic Algorithm (Katoch et al., 2021) (GA) in the year 1970. It is based on a genetic organism's hereditary mechanism. Normal humans developed through many generations according to the idea of normal taxonomy and the “survival of the fittest,” according to Charles Darwin, who claimed that the genesis of living things was undeniable. GA is an adaptive procedure that is used to tackle problems involving search and optimization (Hashim et al., 2021). No more explanation can be found after a large number of new generations have been created using the outlined technique. This answer is regarded as the ultimate outcome.

The vast amount of data we have access to can be separated into tiny groupings, each of which may be assessed as a population. Re-applying hereditary operators to the populace is the most beneficial approach to the present predicament. As we all know, search progress is a problem-solving process in which we can't predict the sequence of actions that will lead to interpretation in future publications. Based on how well and smartly we employed search operators to accomplish this goal. A good search mechanism should be capable of doing searches both locally and in a random manner. Local search investigates all local capabilities and helps to reach the best solution as far as possible, while random search (Wu et al., 2021) explores the whole solution and is effective at avoiding the most favourable local.

Figure 1.

Genetic algorithm flow chart

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Procedure

  • 1.

    Use the Genetic K-Means Algorithm

We may have an algorithm that combines the progress of the genetic algorithm with the K-Means method for clustering, in addition to parallel implementation utilising genetic algorithms. Optimal clustering (Pramanik et al., 2020) is more natural than K-Means clustering, however it typically comes with certain short-term drawbacks.

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Analysis Of Primary Components (Pca)

In this case, the data dimension is given additional weight in order to facilitate computation. Let's examine at two-dimensional data to see how PCA (Pramanik et al., 2021) works. When data is plotted on a graph, two axes are created. PCA works with data, which is subsequently transformed into a single dimension. This is shown in Figure 1, and PCA pseudo-code is presented in Figure 2.

Figure 2.

Shows data visualisation before and after PCA

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Figure 3.

Pseudocode for PCA

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