abstract = "One of the obstacles to Evolutionary Algorithms (EAs)
fulfilling their promise as easy to use general-purpose
problem solvers, is the difficulty of correctly
configuring them for specific problems such as to
obtain satisfactory performance. Having a mechanism for
automatically configuring parameters and operators of
every stage of the evolutionary life-cycle would give
EAs a more widely spread popularity in the non-expert
community. This paper investigates automatic
configuration of one of the stages of the evolutionary
life-cycle, the parent selection, via a new concept of
semi-autonomous parent selection, where mate selection
operators are encoded and evolved as in Genetic
Programming. We compare the performance of the EA with
semi-autonomous parent selection to that of a manually
configured EA on three common test problems to
determine the 'price' we pay for user-friendliness.",
notes = "PHD \cite{EkaterinaHoldenerDissertation} MST 2008 The
art of parameterless evolutionary algorithms
http://web.mst.edu/~tauritzd/nc-lab/EkaterinaHoldenerDissertation.pdf
http://laurel.lso.missouri.edu/search~S8?/tMST+thesis.+Computer+Science+%28Ph.D.%2C+2008%29./tmst+thesis+computer+science+ph+d+2008/-3%2C-1%2C0%2CB/frameset&FF=tmst+thesis+computer+science+ph+d+2008&1%2C%2C6
CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET. IEEE Catalog Number: 07TH8963C",