  Solving many-objective problems (MaOPs) is still a significant challenge in
the multi-objective optimization (MOO) field. One way to measure algorithm
performance is through the use of benchmark functions (also called test
functions or test suites), which are artificial problems with a well-defined
mathematical formulation, known solutions and a variety of features and
difficulties. In this paper we propose a parameterized generator of scalable
and customizable benchmark problems for MaOPs. It is able to generate problems
that reproduce features present in other benchmarks and also problems with some
new features. We propose here the concept of generative benchmarking, in which
one can generate an infinite number of MOO problems, by varying parameters that
control specific features that the problem should have: scalability in the
number of variables and objectives, bias, deceptiveness, multimodality, robust
and non-robust solutions, shape of the Pareto front, and constraints. The
proposed Generalized Position-Distance (GPD) tunable benchmark generator uses
the position-distance paradigm, a basic approach to building test functions,
used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ),
Walking Fish Group (WFG) and others. It includes scalable problems in any
number of variables and objectives and it presents Pareto fronts with different
characteristics. The resulting functions are easy to understand and visualize,
easy to implement, fast to compute and their Pareto optimal solutions are
known.

Neural and Evolutionary Computing;Artificial Intelligence