Hyperparameter Optimization With Genetic Algorithms In Kotlin

Hyperparameter Optimization With Genetic Algorithms In Kotlin

Neural Networks meet Charles Darwin’s Principle

Genetic Algorithms (GAs) are a subset of Evolutionary Algorithms, which use mechanisms inspired by nature such as mutation, evolution, reproduction, etc. In spite of using advanced math, these algorithms work on principles which are derived from nature. Growth of an organism in its environment and its interaction with other beings in that environment can be modeled with simple mathematical expressions.

Today, we’ll perform Hyperparameter Optimization with GAs to see how neural networks can be treated as individuals living in a population where each one has its own set of genes (which are actually hyperparameters).

Hyperparameter optimizations consist of those techniques through which we can determine the best combination of hyperparameters (constant parameters which are to be given to the model) so as to have the best results from the model. A “best combination” would mean a set of hyperparameters which give the least value for our loss function in one single iteration. With the help of GAs, we’ll evolve various sets of hyperparameters using Charles Darwin’s principle of Natural Selection. It’s quite famous in biology textbooks, but essentially deems that the ability to evolve is what carries on from generation to generation and that traits will be selected for their usefulness.

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