  The Travelling Salesman and its variations are some of the most well known NP
hard optimisation problems. This paper looks to use both centralised and
decentralised implementations of Evolutionary Algorithms (EA) to solve a
dynamic variant of the Multi-Agent Travelling Salesman Problem (MATSP). The
problem is dynamic, requiring an on-line solution, whereby tasks are completed
during simulation with new tasks added and completed ones removed. The problem
is allocating an active set of tasks to a set of agents whilst simultaneously
planning the route for each agent. The allocation and routing are closely
coupled parts of the same problem making it difficult to decompose, instead
this paper uses multiple populations with well defined interactions to exploit
the problem structure. This work attempts to align the real world
implementation demands of a decentralised solution, where agents are far apart
and have communication limits, to that of the structure of the multi-demic EA
solution process, ultimately allowing decentralised parts of the problem to be
solved `on board' agents and allow for robust communication and exchange of
tasks.
