AI News, Why is Genetic Programming not popular in machine learning?

Why is Genetic Programming not popular in machine learning?

Main advantage of evolutionary technique is its ability to get global optimum in a parallel framework, even as an outsider of the original problem.

Progress in GPU research with availability of digital data, facilitates us to attack relatively difficult machine learning problems (vision, speech, language) in recent years, where dimension of the problem space is very high.

On contrary, automatic differentiation aided gradient based techniques are quite successful in training for high dimensional problems without sticking-up inside a local minimum, as upward slopes in all the dimensions, which is required for local minimum is extremely rare.

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What is Optimization? + Learning Gradient Descent | Two Minute Papers #82

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Gradient Descent - Artificial Intelligence for Robotics

This video is part of an online course, Intro to Artificial Intelligence. Check out the course here:

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Learn the Multi-Dimensional Gradient Method of optimization via an example. Minimize an objective function with two variables (part 1 of 2).

Natalie Hockham: Machine learning with imbalanced data sets

Classification algorithms tend to perform poorly when data is skewed towards one class, as is often the case when tackling real-world problems such as fraud ...

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Lecture 3 | Loss Functions and Optimization

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4.3 Gradient-Based Optimization (Part 1)

Book: Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.