Compact Optimization Algorithms with Re-sampled Inheritance
  Compact optimization algorithms are a class of Estimation of Distribution
Algorithms (EDAs) characterized by extremely limited memory requirements (hence
they are called "compact"). As all EDAs, compact algorithms build and update a
probabilistic model of the distribution of solutions within the search space,
as opposed to population-based algorithms that instead make use of an explicit
population of solutions. In addition to that, to keep their memory consumption
low, compact algorithms purposely employ simple probabilistic models that can
be described with a small number of parameters. Despite their simplicity,
compact algorithms have shown good performances on a broad range of benchmark
functions and real-world problems. However, compact algorithms also come with
some drawbacks, i.e. they tend to premature convergence and show poorer
performance on non-separable problems. To overcome these limitations, here we
investigate a possible algorithmic scheme obtained by combining compact
algorithms with a non-disruptive restart mechanism taken from the literature,
named Re-Sampled Inheritance (RI). The resulting compact algorithms with RI are
tested on the CEC 2014 benchmark functions. The numerical results show on the
one hand that the use of RI consistently enhances the performances of compact
algorithms, still keeping a limited usage of memory. On the other hand, our
experiments show that among the tested algorithms, the best performance is
obtained by compact Differential Evolution with RI.
