Introducing H-Leading-Ones as a Mixed-Category Benchmark Problem for Evolutionary Algorithms

In the wake of generative artificial intelligence and the exponential growth in the volume of data generated, the associated increase in data complexity in the sense of the quantity of different data types present in a single system poses a challenge to evolutionary algorithms. To allow for the development and testing of new algorithms adapted to this new data landscape, test problems are necessary as a way to both evaluate and compare algorithms performances. However, while recent advances extended known test problems such as the r-Leading-Ones marking the transition from binary to multi-valued variables, having different data-types coexisting in the search space is still an open question. We propose the h-Leading-Ones as an extension of the r-Leading-Ones to evaluate the ability of an algorithm to solve problems on a search space composed of multi-valued and real-valued data types. Its design with dependency between the different data-types and its continuity with the r-Leading-Ones provides a convenient new environment for benchmark and runtime analysis for mixed-category search spaces.