Inproceedings,
Beyond ``Genetic Programming'': Incremental Self-Improvement
Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, page 42--49. Tahoe City, California, USA, (9 July 1995)
Abstract
Back in 1986, Dickmanns, Winklhofer, and the author
used a genetic algorithm to evolve variable-length
computer programs 4. Today, our approach would be
classified as "Genetic Programming" (GP). We
applied it to simple tasks, including the "lawnmower
problem" (later also studied by Koza, 1994). In
subsequent work (1987 --- 1994), we found GP
unsatisfactory for many reasons: (1) GP's way of
constructing new code from old code does not improve
itself: it always remains limited to the initial
crossover and mutation mechanisms. (2) Like almost all
other learning paradigms, GP requires concepts that are
unrealistic in real world applications, such as
"resettable environments and exactly repeatable
trials". In general, however, realistic environments
cannot be reset -- time is one-way, and there is only
one single lifelong training sequence. (3) Like almost
all other learning paradigms, GP's objective function
does not take into account the computation time
required for learning itself. To...
