Evolution of Ideas: A Novel Memetic Algorithm Based on Semantic Network
Publication Type:
Conference PaperSource:
IEEE Congress on Evolutionary Computation, IEEE, Brisbane, p.1-8 (2012)Keywords:
semantic networks; memetic algorithmsAbstract:
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.
Automated Generation of Cross-Domain Analogies via Evolutionary Computation
An Evolutionary Approach to Enhance Data Privacy
Publication Type:
Journal ArticleSource:
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer, Volume 15, Issue 7, p.1301-1311 (2011)URL:
http://www.springerlink.com/content/42357225707508x2/Keywords:
Information Privacy and Security; Evolutionary AlgorithmsAbstract:
Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized dis- closure. Perturbative masking methods propose the distor- tion of the original data sets before publication, tackling a difficult tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk).
In this paper we describe how information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets in order to illustrate and empirically evaluate the application of this technique.
PRAM Optimization Using an Evolutionary Algorithm
Publication Type:
Book ChapterSource:
Privacy in Statistical Databases, Springer, Number LNCS 6344, Corfú, Greece, p.97 - 106 (2010)ISBN:
978-3-642-15837-7Keywords:
Information Privacy and Security; Evolutionary Algorithms; Post Randomization Method; Information Loss; Disclosure RiskAbstract:
PRAM (Post Randomization Method) was introduced in 1997 but it is still one of the least used methods in statistical categorical data protection. This fact is because of the difficulty to obtain a good transition matrix in order to obtain a good protection. In this paper, we describe how to obtain a better protection using an evolutionary algorithm with integrated information loss and disclosure risk measures to find the best matrix. We also provide experiments using a real dataset of 1000 records in order to empirically evaluate the application of this technique.
