Inproceedings,
Computation Process Evolution
2006 IEEE International Conference on Engineering of Intelligent Systems, page 1--6. IEEE, (2006)
DOI: doi:10.1109/ICEIS.2006.1703138
Abstract
Unlike other genetic methods which are devoted to
optimise the input data, this paper proposes an
approach, CPE, aiming at finding the computation
process of any problem by only using a few input and
output data, consisting of the cases needed to be
satisfied and those needed to be avoided. It first
encodes the antibody using the method similar to that
of gene expression programming (GEP), a new efficient
technique of genetic programming (GP) with linear
representation. Through the gradual evolution, the
affinity between antibody and the non-selves become
more and more intense. At the same time, every time
after the chromosomes are mutated, the chromosomes
should be checked to determine whether the antibody
chromosome would match the selves, which are the
conditions that should be satisfied. Two kind of
experiment are examined in order to test the
performance of the approach. The results show that CPE
evolves out the data-processing processes which are
exactly the same as those from which the experimental
input data were generated, and compared with GP and GEP
which is currently one of the most efficient genetic
methods, CPE experiences shorter evolution process.
Most importantly, unlike previous evolutionary methods
that only consider increasing fitness, this approach
takes into account both the goal (fitness) and the
constraints of actual problems, which makes it possible
to solve complex real problems using evolutionary
computation
