Papers by Nguyen Ngoc Hien
In this paper, we describe a new test problem for genetic programming (GP), ORDERTREE. We argue t... more In this paper, we describe a new test problem for genetic programming (GP), ORDERTREE. We argue that it is a natural analogue of ONEMAX, a popular GA test problem, and that it also avoids some of the known weaknesses of other benchmark problems for Genetic Programming. Through experiments, we show that the difficulty of the problem can be tuned not only by increasing the size of the problem, but also by increasing the nonlinearity in the fitness structure.
This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to ... more This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets is also investigated for both standard crossover and SSC. All GP systems are tested on a number of real-valued symbolic regression problems. The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing data. Further recorded statistics shows that the size of the evolved solutions by using SSC are often smaller than ones obtained from GP systems that do not use semantics. This can be seen as one of the reasons for the success of SSC in improving the generalisation ability of GP.
A study on Genetic Programming with layered learning and incremental sampling
In this paper, we investigate the impact of a layered learning approach with incremental sampling... more In this paper, we investigate the impact of a layered learning approach with incremental sampling on Genetic Programming (GP). The new system, called GPLL, is tested and compared with standard GP on twelve symbolic regression problems. While GPLL does not differ from standard GP on univariate target functions, it has better training efficiency on problems with bivariate targets. This indicates the potential usefulness of layered learning with incremental sampling in improving the efficiency of GP evolutionary learning.
In the field of Genetic Programming (GP), there has been a growing interest in the effects of los... more In the field of Genetic Programming (GP), there has been a growing interest in the effects of loss of genetic diversity, which causes the whole population prematurely converge to local optima. Improving diversity of the population is always an implicit goal of almost any basic genetic programming system. Most research in this area suggests a diversity measurement and controls this quantitative metric to maintain genetically diverse populations. This paper brief overviews of the measures used in Genetic Programming for diversity maintenance and promotion.
Journal of Experimental Nanoscience, 2008
Journal of Experimental Nanoscience, 2010
Uploads
Papers by Nguyen Ngoc Hien