1 research outputs found
Influence of Topological Features on Spatially-Structured Evolutionary Algorithms Dynamics
In the last decades, complex networks theory significantly influenced other
disciplines on the modeling of both static and dynamic aspects of systems
observed in nature. This work aims to investigate the effects of networks'
topological features on the dynamics of an evolutionary algorithm, considering
in particular the ability to find a large number of optima on multi-modal
problems. We introduce a novel spatially-structured evolutionary algorithm and
we apply it on two combinatorial problems: ONEMAX and the multi-modal NMAX.
Considering three different network models we investigate the relationships
between their features, algorithm's convergence and its ability to find
multiple optima (for the multi-modal problem). In order to perform a deeper
analysis we investigate the introduction of weighted graphs with time-varying
weights. The results show that networks with a large Average Path Length lead
to an higher number of optima and a consequent slow exploration dynamics (i.e.
low First Hitting Time). Furthermore, the introduction of weighted networks
shows the possibility to tune algorithm's dynamics during its execution with
the parameter related with weights' change. This work gives a first answer
about the effects of various graph topologies on the diversity of evolutionary
algorithms and it describes a simple but powerful algorithmic framework which
allows to investigate many aspects of ssEAs dynamics.Comment: Submitted to IEEE Transactions on Evolutionary Computation, 14 pages,
14 figure