  Comparison-Based Optimization (CBO) is an optimization paradigm that assumes
only very limited access to the objective function f(x). Despite the growing
relevance of CBO to real-world applications, this field has received little
attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO).
In this work we propose a relatively simple method for converting ZOO
algorithms to CBO algorithms, thus greatly enlarging the pool of known
algorithms for CBO. Via PyCUTEst, we benchmarked these algorithms against a
suite of unconstrained problems. We then used hyperparameter tuning to
determine optimal values of the parameters of certain algorithms, and utilized
visualization tools such as heat maps and line graphs for purposes of
interpretation. All our code is available at
https://github.com/ishaslavin/Comparison_Based_Optimization.
