  Differential evolution (DE) is a well-known type of evolutionary algorithms
(EA). Similarly to other EA variants it can suffer from small populations and
loose diversity too quickly. This paper presents a new approach to mitigate
this issue: We propose to generate new candidate solutions by utilizing
reversible linear transformation applied to a triplet of solutions from the
population. In other words, the population is enlarged by using newly generated
individuals without evaluating their fitness. We assess our methods on three
problems: (i) benchmark function optimization, (ii) discovering parameter
values of the gene repressilator system, (iii) learning neural networks. The
empirical results indicate that the proposed approach outperforms vanilla DE
and a version of DE with applying differential mutation three times on all
testbeds.
