abstract = "Recently, a form of memory usage was introduced for
genetic programming (GP) called {"}soft memory.{"}
Rather than have a new value completely overwrite the
old value in a register, soft memory combines the new
and old register values. This work examines the
performance of a soft memory linear GP and
developmental GP implementation for stock trading. Soft
memory is known to more slowly adapt solutions compared
to traditional GP. Thus, it was expected to perform
well on stock data which typically exhibit local
turbulence in combination with an overall longer term
trend. While soft memory and standard memory were both
found to provide similar impressive accuracy in buys
that produced profit and sells that prevented losses,
the softer memory settings traded more actively. The
trading of the softer memory systems produced less
substantial cumulative gains than traditional memory
settings for the stocks tested with climbing share
price trends. However, the trading activity of the
softer memory settings had moderate benefits in terms
of cumulative profit compared to buy-and-hold strategy
for share price trends involving a drop in prices
followed later by gains.",
notes = "GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).