EdgeWare FastBreak Pro Version 5 - Manual (Page 79)

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would be found during the IS optimization without robustness because the robustness
check is yet another constraint that the genetic algorithm needs to satisfy. Adding
constraints usually results in a reduction to the variable being optimized (constraints such
as MDD, or switches per year have the same effect). We are willing to pay a small price
in IS performance if it results in a system that has better OS performance, i.e., better
predictive ability. What is probably happening in the above example is that the
robustness is preventing the genetic algorithm from converging too rapidly. Trying to
satisfy the robustness constraint is similar to the effect of mutation. Preventing too rapid
of a convergence forces the genetic algorithm to do a better job of examining the total
trade space (“trade space” is a way of saying full range of parameters) . This results in
finding better parameter solutions.
In some cases, you may see a significant difference in performance with a small
percentage change in parameters. For example, you may have a small trading family
with only four members. Using a Top% value of 59%, the sell point is reached when a
fund drops out of the top two ranking positions (0.59 X 4 = 2.4 which FastBreak Pro
rounds to 2). When the 59% Top% is increased by 10% the sell point requires the fund
to drop out of the top three ranking positions (0.59 X 1.1 X 4 = 2.6 which FastBreak Pro
rounds to 3). You must be aware of these types of effects.
It may be emotionally difficult to select trading systems that use the robustness check and
do not perform as well compared to systems that skip the robustness check. However,
the majority of trading systems developers would argue that systems sensitive to small
parameter changes should not be trusted.