Publication Type:
Conference Paper
Source:
IEEE Congress on Evolutionary Computation, IEEE, Brisbane, p.1-8 (2012)
Keywords:
semantic networks;
memetic algorithms
Abstract:
This paper presents a new type of evolutionary
algorithm (EA) based on the concept of “meme”, where the
individuals forming the population are represented by semantic
networks and the fitness measure is defined as a function of the
represented knowledge. Our work can be classified as a novel
memetic algorithm (MA), given that (1) it is the units of culture,
or information, that are undergoing variation, transmission,
and selection, very close to the original sense of memetics as
it was introduced by Dawkins; and (2) this is different from
existing MA, where the idea of memetics has been utilized as a
means of local refinement by individual learning after classical
global sampling of EA. The individual pieces of information are
represented as simple semantic networks that are directed graphs
of concepts and binary relations, going through variation by
memetic versions of operators such as crossover and mutation,
which utilize knowledge from commonsense knowledge bases.
In evaluating this introductory work, as an interesting fitness
measure, we focus on using the structure mapping theory of
analogical reasoning from psychology to evolve pieces of information
that are analogous to a given base information. Considering
other possible fitness measures, the proposed representation and
algorithm can serve as a computational tool for modeling memetic
theories of knowledge, such as evolutionary epistemology and
cultural selection theory.
