  Evolutionary multi-agent systems (EMASs) are very good at dealing with
difficult, multi-dimensional problems, their efficacy was proven theoretically
based on analysis of the relevant Markov-Chain based model. Now the research
continues on introducing autonomous hybridization into EMAS. This paper focuses
on a proposed hybrid version of the EMAS, and covers selection and introduction
of a number of hybrid operators and defining rules for starting the hybrid
steps of the main algorithm. Those hybrid steps leverage existing, well-known
and proven to be efficient metaheuristics, and integrate their results into the
main algorithm. The discussed modifications are evaluated based on a number of
difficult continuous-optimization benchmarks.
