Knowledge Representation in Transfer Optimisation System and Applications for Highly Configurable Software Systems

This project plan to develop transfer optimisation algorithms that combines the idea between nature-inspired optimisation and transfer learning to equip an optimisation algorithm with adequate intelligence thus lead to a self-adaptive behaviour. To this end, the research will focus on one of the key questions to the endeavour of transfer optimisation, i.e., the knowledge representation and metrics used to evaluate and compare the similarity between different "experience" learned from the previous optimisation process. By doing so, it is able to overcome the negative transfer which brings disasters to transfer irrelevant or useless knowledge across tasks.

We will start from graph theory given that graph is a general but powerful representation to various structures. In this case, we envisage that it is able to be the building block for knowledge representation for various landscapes. Representation learning techniques, which learn the intrinsic structure and representation of the data to facilitate useful information extraction, will be developed to understand the problem features from the representation of the fitness landscape itself. As for continuous variables, I will study to use reconstruction-based approaches that learns a parametric mapping from observed data to a representation like the autoencoder framework. For discrete variables, I will study to represent the fitness landscape as an information network. Then network representation learning approaches will be developed to learn a latent low-dimensional representations of network vertices while preserving network topology structure. In order to measure the similarity between different knowledge, I will develop some metrics to serve the quantitative evaluation. This is essentially related to the way how the knowledge is represented.For the knowledge represented as a low-dimensional encoder, I will evaluate similarity based on standard distance measures like Euclidean distance. For the knowledge represented as an information network, I will study from the graph matching perspective [2] and to develop similarity functions to measure the structural similarity between different networks.Once the knowledge representation and similarity measure are developed. I will study how to use them within nature-inspired computation to come up with a transfer optimisation algorithm.Many transfer learning techniques in the machine learning literature [5] are able to serve the purpose of transfer learning. In particular, I will consider two levels of knowledge transfer. One is genetic-level which aims to leverage the optima found in the previous optimisation exercises to accelerate the underlying optimisation. The other one is model level which is going to use transfer learning techniques to align the models across various tasks.

The transfer optimisation algorithms developed in this project will be applied to optimise the non-functional performance of highly configurable software systems. Modern industrial software systems are super complex with many configuration options, the setting of which is directly related to their non-functional performance. It is arguable that those systems are too complex to be manually configured in order to achieve their peak performance at runtime under various environments and different user requirements. It is also time consuming to evaluate the non-functional performance of the underlying system when it incurs the throughput of huge volume of data. Building a surrogate to understand and predict the effect of a configuration option is promising alternative to enable the optimisation of a self-adaptive software system at runtime. More specifically, the knowledge representation developed in this PhD project will serve the purpose of surrogate modelling whilst the transfer optimisation will be used to learn and accumulate knowledge through optimisation.