Brian J. Ross research info


Professor
Dept of Computer Science
Brock University

Member of Bio-Inspired Computational Intelligence Group.

Teaching

Curriculum vitae (pdf)

Education

Research interests

Student supervision

MSc 3P99/4F90 projects

Research statement

My doctoral research involved the use of process algebra for modeling logic programming language control. This semantics could be used as a framework for proving program termination and transformation properties of Prolog programs, including those with cuts. It could also be used to define the operational semantics of logic program control strategies other than Prolog, such as those with mixed sequential and concurrent control. Since that time, much of my research has involved the practical application of process algebra for a variety of uses, for example, the automatic synthesis of stochastic networks.

Currently, my research is focussed on the use of machine learning techniques for formal language induction and concurrent software synthesis. Initially, I developed some language induction algorithms for algebras with interleaving. Although the algorithms derived had polynomial complexity, the process algebra used as a target language was not too robust. Further research showed that genetic programming is an excellent means for automatically synthesizing process algebraic systems. I first used a CCS-like process algebra as the target language, and developed a Prolog-based GP system for evolving CCS expressions solving a variety of concurrent problems. I am now studying new evolutionary computation techniques for richer concurrent languages, as well as for stochastic formal languages such as stochastic regular expressions. Recently, there has been a lot of interest in the stochastic pi-calculus, and I have been investigating the automatic synthesis of networks encoded in that formalism. Eventually, I hope to apply this research towards real-world problems in bioinformatics, for example, the automatic derivation of bionetworks.

To support my research, I developed a logic-grammar-based genetic programming system called DCTG-GP (Definite Clause Translation Grammar for Genetic Programming). DCTG-GP lets the user define their target language using a context--free attribute grammar. This environment permits the languages grammar and semantics to be unified together, and also permits syntactic and semantic constraints to be conveniently encoded.

I also have an interest in applications of evolutionary computation in computer graphics and design. The Gentropy system I have developed with students synthesizes 2D textures that match various feature characteristics of one or more target images - all without human supervision. Suites of rudimentary image analysis tests rank the suitability of candidate textures. Different implementations of the system have used multiple populations and multi-objective search. The latest system incorporates a mathematical model of aesthetics, with the goal of evolving visually pleasing images. One project investigated the evolution of image filters, which attempt to duplicate a target colour palette, while adhering to the aesthetic model. Other research has used genetic programming to evolve procedural textures for 3D surfaces. This system uses training examples in order to evolve textures that conform to the surface characteristics of 3D models. Example results from these research topics are on my web site below.

Evolutionary design and art

Evolving 3D procedural textures using genetic programming
with Adam Hewgill

Evolving 2D textures using a model of aesthetics
with Bill Ralph and Hai Zong

Evolving 2D image filters
with Craig Neufeld and Bill Ralph

Evolving 2D vectorized images
with Steven Bergen

Theses

Book chapters

Journals

Conferences

Technical reports



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