abstract = "Classical genetic programming solves problems by
applying the Darwinian concepts of selection, survival
and reproduction to a population of computer programs.
Here we extend the biological analogy to incorporate
epigenetic regulation through both learning and
evolution. We begin the chapter with a discussion of
Darwinian, Lamarckian, and Baldwinian approaches to
evolutionary computation and describe how recent
findings in biology differ conceptually from the
computational strategies that have been proposed. Using
inheritable Lamarckian mechanisms as inspiration, we
propose a system that allows for updating of
individuals in the population during their lifetime
while simultaneously preserving both genotypic and
phenotypic traits during reproduction. The
implementation is made simple through the use of
syntax-free, developmental, linear genetic programming.
The representation allows for arbitrarily-ordered
genomes to be syntactically valid programs, thereby
creating a genetic programming approach upon which
quasi-uniform epigenetic updating and inheritance can
easily be applied. Generational updates are made using
an epigenetic hill climber (EHC), and the epigenetic
properties of genes are inherited during crossover and
mutation. The addition of epigenetics results in faster
convergence, less bloat, and an improved ability to
find exact solutions on a number of symbolic regression
problems.",