Contents
Is genetic algorithm used in machine learning?
A genetic algorithm is a search-based algorithm used for solving optimization problems in machine learning. This algorithm is important because it solves difficult problems that would take a long time to solve.
Is genetic algorithm reinforcement learning?
Reinforcement Learning enables agents to take decision based on a reward function. Using a Genetic Algorithm to find the values of parameters used in the learning algorithm, let’s say Deep Deterministic Policy Gradient (DDPG) combined with Hindsight Experience Replay (HER), to help speed up the learning agent.
What are the disadvantages of genetic algorithm?
Disadvantages of Genetic Algorithm
- GA implementation is still an art.
- GA requires less information about the problem, but designing an objective function and getting the representation and operators right can be difficult.
- GA is computationally expensive i.e. time-consuming.
How are genetic algorithms used in machine learning?
Genetic algorithms provide a powerful technique for hyperparameter tuning, but they are quite often overlooked. In this article, I will show an overview of genetic algorithms. I will also offer a detailed step-by-step guide on exploiting available libraries to use genetic algorithms to optimize the hyperparameters of a Machine Learning model.
How is the genetic algorithm used in hyperparameter tuning?
The time-consuming process of hyperparameter tuning. The idea of the Genetic Algorithm is to gain the optimal solutions of the objective function by selecting the best or fittest solution alongside the rare and random mutation occurrence.
How does the genetic algorithm in are work?
The idea of the Genetic Algorithm is to gain the optimal solutions of the objective function by selecting the best or fittest solution alongside the rare and random mutation occurrence. For those who want to understand how the algorithm works, I created an article that explains the algorithm’s concept here.
How is hyperparameter tuning performed in ML model?
Traditionally, hyperparameter tuning in the ML model is usually performed by trial and error process. Depending on how many hyperparameters exist in the ML model, this process can be very exhausting, especially when we working with large data.