Random Search

Introduction

The random search explores by choosing a new position at random after each iteration. Some random search implementations choose a new position within a large hypersphere around the current position. The implementation in hyperactive is purely random across the search space in each step.

from hyperactive import Hyperactive
from hyperactive.optimizers import RandomSearchOptimizer

...

optimizer = RandomSearchOptimizer()

hyper = Hyperactive()
hyper.add_search(model, search_space, n_iter=50, optimizer=optimizer)
hyper.run()

About the implementation

The random search is a very simple algorithm that has no parameters to change its behaviour. In each iteration the random position is selected via random.choice from a list of possible positions.