Discussing the article: "Population ADAM (Adaptive Moment Estimation)"

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Check out the new article: Population ADAM (Adaptive Moment Estimation).

The article presents the transformation of the well-known and popular ADAM gradient optimization method into a population algorithm and its modification with the introduction of hybrid individuals. The new approach allows creating agents that combine elements of successful decisions using probability distribution. The key innovation is the formation of hybrid population individuals that adaptively accumulate information from the most promising solutions, increasing the efficiency of search in complex multidimensional spaces.

In 2014, two outstanding minds, D. P. Kingma and J. Ba proposed the ADAM algorithm, which combines the best features of its predecessors, such as AdaGrad and RMSProp. The algorithm was specifically designed to optimize the weights of neural networks using the gradients of the activation functions of neurons. It is based on adaptive first and second moment estimates, making it simple to implement and highly computationally efficient. The algorithm requires minimal memory resources and does not depend on diagonal rescaling of gradients, which makes it particularly suitable for problems with large amounts of data and parameters.

ADAM also performs well on non-stationary targets and situations where gradients may be noisy or sparse. The algorithm hyperparameters are easy to interpret and usually do not require complex tuning.

However, despite its efficiency in the field of neural networks, ADAM is limited to the use of analytical gradients, which narrows the range of its applications. In this article, we propose an innovative approach to modifying the ADAM algorithm by transforming it into a population-based optimization algorithm capable of handling numerical gradients. This modification not only extends the scope of ADAM beyond neural networks, but also opens up new possibilities for solving a wide range of optimization problems in general.

Our research aims to create a general-purpose optimizer that retains the benefits of the original ADAM but can operate effectively in settings where analytical gradients are not available. This will allow the modified ADAM to be applied in areas such as global optimization and multi-objective optimization, significantly expanding its potential and practical value.


Author: Andrey Dik

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