  In practical optimisation the dominant characteristics of the problem are
often not known prior. Therefore, there is a need to develop general solvers as
it is not always possible to tailor a specialised approach to each application.
The hybrid form of Multi-Level Selection Genetic Algorithm (MLSGA) already
shows good performance on range of problems due to its diversity-first
approach, which is rare among Evolutionary Algorithms. To increase the
generality of its performance this paper proposes a distinct set of
co-evolutionary mechanisms, which defines co-evolution as competition between
collectives rather than individuals. This distinctive approach to
co-evolutionary provides less regular communication between sub-populations and
different fitness definitions between individuals and collectives. This
encourages the collectives to act more independently creating a unique
sub-regional search, leading to the development of co-evolutionary MLSGA
(cMLSGA). To test this methodology nine genetic algorithms are selected to
generate several variants of cMLSGA, which incorporates these approaches at the
individual level. The new mechanisms are tested on over 100 different functions
and benchmarked against the 9 state-of-the-art competitors in order to find the
best general solver. The results show that the diversity of co-evolutionary
approaches is more important than their individual performances. This allows
the selection of two competing algorithms that improve the generality of
cMLSGA, without large loss of performance on any specific problem type. When
compared to the state-of-the-art, the proposed methodology is the most
universal and robust, leading to an algorithm more likely to solve complex
problems with limited knowledge about the search space.
