Yinan Shan

Learn More
In Genetic Programming (GP) and most of the other evolutionary computing approaches, the knowledge which is learned during the evolutionary processing is implicitly encoded in the population. In this research, we proposed a new approach for program synthesis – Program Evolution with Explicit Learning (PEEL), which learns and makes use of this knowledge(More)
This research extends conventional Estimation of Distribution Algorithms (EDA) to Genetic Programming (GP) domain. We propose a framework to estimate the distribution of solutions in tree form. The core of this framework is a grammar model. In this research, we show, both theoretically and experimentally, that a grammar model has many of the properties we(More)
This paper reports on research using a variety of machine learning techniques to a difficult modelling problem, the spatial distribution of an endangered Australian marsupial, the southern brown bandicoot (Isoodon obesulus). Four learning techniques – decision trees/rules, neural networks, support vector machines and genetic programming – were applied to(More)
AntTAG in [4], which combines ant search and GGGP, is a promising new method for program automatic synthesis. This paper studied the behavior of AntTAG on a specific symbolic regression problem. Based on observations, we slightly tailored AntTAG for this specific problem. This tailored version showed impressive performance. Experiments using AntTAG on more(More)
In Genetic Programming (GP) and most other evolutionary computing approaches, the knowledge learned during the evolutionary processing is implicitly encoded in the population. A small family of approaches, known as Estimation of Distribution Algorithms , learn this knowledge directly in the form of probability distributions. In this research, we proposed a(More)
The theme of this paper is that anomaly detection splits into two parts: developing the right features, and then feeding these features into a statistical system that detects anomalies in the features. Most literature on anomaly detection focuses on the second part. Our goal is to illustrate the importance of the first part. We do this with two real-life(More)
  • 1