What is Genetic Algorithm in Machine Learning — Simple Explanation
Introduction
Machine Learning techniques have simplified our lives in many ways. But, they also face many issues such as the problem of optimization. However, there are effective solutions like a genetic algorithm to overcome this challenge. The genetic algorithm in machine learning is a search-based algorithm for solving optimization problems. It is also useful in various real-life applications such as data centers and image processing.
This article will let you know the basics of GA and how it operates. It also describes its applications in many industries and identifies some of its drawbacks.
What is a Genetic Algorithm?
A genetic algorithm is a technique popular in operations research and computer science. It works to tackle optimization problems by mimicking the natural selection process. Additionally, a genetic algorithm uses mutation, cross-over, and selection to produce the best result.
Therefore, the goal is to begin with a large population and then steadily diminish it. So, in the end, you are left with the best.
Genetic Algorithm in Machine Learning
There are several use cases of these Algorithms in ML such as feature selection and Model Hyper-parameter Tuning. ML Pipeline Optimization is also a common use case of a genetic algorithm in machine learning. This is because GA is a heuristic search method that can tackle search and optimization issues.
Additionally, GA uses the idea of genetics and natural selection to resolve issues. These algorithms are more intelligent than random search algorithms. They also enhance the search results using historical data.
How does a Genetic Algorithm in ML Work?
GA uses the evolutionary generational cycle to provide the best solutions. They also employ various methods to increase or replace the population and give a suitable solution.
They follow the below phases to solve complex optimization issues:
- Initialization
The entire process begins with creating a group of people known as the Population.
- Fitness Function
The fitness function aims to determine the population’s overall level of fitness.
- Selection
It involves choosing a member of the current generation based on fitness to breed the next generation. Thus, the selection of the most suitable sample data is a crucial step in genetic machine learning.
- Reproduction
This phase involves the development of a child population. The strategy uses parent population variation operators.
- Replacement
This phase involves generational replacement. Thus, the new child population replaces the old population.
- Termination
A terminating factor acts as a basis for termination after replacement.
Advantages of Genetic Programming in Machine Learning
- GA provides a remedy for an issue that gets better with time.
- Genetic algorithms also provide the best parallel capabilities.
- They can optimize many issues, like discrete functions, and continuous functions.
- Derivative information is not necessary for a genetic algorithm.
Limitations of Genetic Algorithm in Machine Learning
- They are ineffective at resolving simple issues.
- An improper implementation can result in wrong output.
- There is no insurance for the final product’s quality.
- Some tasks may face computing difficulties due to the repetitive calculation of fitness values.
Applications of Genetic Algorithm
Genetic learning in AI and ML has below applications:
1. Image processing
One of the common uses of genetic optimization is image processing tasks. Moreover, they are applicable in every step to simplify the image analysis.
2. Neural networks
Neural networks in machine learning are a suitable application of genetic programming.
3. Wireless sensor network
A wireless sensor network has dedicated and dispersed centers. These centers keep track of the physical conditions of an environment
4. Vehicle routing
The vehicle routing problem is one of the common traveling salesman problems. However, Genetic algorithms help find the ideal weight of items you need to transport. Thus, it uses a suitable combination of delivery routes.
5. Financial Industry
In the financial market, genetic optimization can address a range of problems. It also helps in determining the ideal set of variables that can impact trades or market regulations.
Conclusion
Genetic algorithm in machine learning are a part of the family of evolutionary algorithms. GA is better than random search algorithms as they use historical data to offer the best option. We have also described its operation, applications, and benefits in this article. For more technical updates, stay tuned to The IoT Academy
