John Mark Swafford’s research while affiliated with University College Dublin and other places

Being able to exploit modularity in genetic programming (GP) is an open issue and a promising vein of research. Previous work has identified a variety of methods of finding and using modules, but little is reported on how the modules are being used in order to yield the observed performance gains. In this work, multiple methods for identifying modules are applied to some common, dynamic benchmark problems. Results show there is little difference in the performance of the approaches. However, trends in how modules are used and how “good” individuals use these modules are seen. These trends indicate that discovered modules can be used frequently and by good individuals. Further examination of the modules uncovers that useful as well as unhelpful modules are discovered and used frequently. The results suggest directions for future work in improving module manipulation via crossover and mutation and module usage in the population.

Modularity has been an important vein of research in evolutionary algorithms. Past research in evolutionary computation has shown that techniques able to decompose the benchmark problems examined in this work into smaller, more easily solved, sub-problems have an advantage over those which do not. This work describes and analyzes a number of approaches to discover sub-solutions (modules) in the grammatical evolution algorithm. Data from the experiments carried out show that particular approaches to identifying modules are better suited to certain problem types, at varying levels of difficulty. The results presented here show that some of these approaches are able to significantly outperform standard grammatical evolution and grammatical evolution using automatically defined functions on a subset of the problems tested. The results also point to a number of possibilities for extending this work to further enhance approaches to modularity.

Evolutionary methods afford a productive and creative alternative design workflow. Crucial to success is the choice of formal representation of the problem. String-rewriting context-free grammars (CFGs) are one common option in evolutionary computation, but their suitability for design is not obvious. Here, a CFG-based evolutionary algorithm for design is presented. The process of meta-design is described, in which the CFG is created and then refined to produce an improved design language. CFGs are contrasted with another grammatical formalism better known in architectural design: Stiny's shape grammars. The advantages and disadvantages of the two types of grammars for design tasks are discussed.

Modularity has proven to be an important aspect of evolutionary computation. This work is concerned with discovering and using modules in one form of grammar-based genetic programming, grammatical evolution (GE). Previous work has shown that simply adding modules to GE's grammar has the potential to disrupt fit individuals developed by evolution up to that point. This paper presents a solution to prevent the disturbance in fitness that can come with modifying GE's grammar with previously discovered modules. The results show an increase in performance from a previously examined grammar modification approach and also an increase in performance when compared to standard GE.

There have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification with a grammar-based approach to genetic programming, grammatical evolution (GE). Here, GE’s grammar was modified over the course of an evolutionary run with modules in order to facilitate their appearance in the population. This is the first step in what will be a series of analysis on methods of modifying GE’s grammar to enhance evolutionary performance. The results show that identifying modules and using them to modify GE’s grammar can have a negative effect on search performance when done improperly. But, if undertaken thoughtfully, there are possible benefits to dynamically enhancing the grammar with modules identified during evolution.

This work furthers the understanding of modularity in grammar-based genetic programming approaches by analyzing how different grammars may be capable of producing the same phenotypes, but still display differences in performance on the same problems. This is done by creating four grammars with varying levels of modularity and using them with grammatical evolution to evolve floor plan designs. The results of this experimentation show how increases in modularity, brought about by simple modifications in the grammars, and increases in the quality of solutions go hand in hand. It also demonstrates how more modular grammars explore more individuals even while fitness remains the same or changes in only minor increments.

In this work, we examine the capabilities of two forms of mappings by means of Grammatical Evolution (GE) to successfully generate controllers by combining high-level functions in a dynamic environment. In this work we adopted the Ms. Pac-Man game as a benchmark test bed. We show that the standard GE mapping and Position Independent GE (πGE) mapping achieve similar performance in terms of maximising the score. We also show that the controllers produced by both approaches have an overall better performance in terms of maximising the score compared to a hand-coded agent. There are, however, significant differences in the controllers produced by these two approaches: standard GE produces more controllers with invalid code, whereas the opposite is seen with πGE.

The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order functions, and describe their general advantages in EC encodings. We implement grammars using higher-order ideas for two problem domains, music and 3D architectural design, and use these grammars in the grammatical evolution paradigm. We demonstrate four advantages of higher-order functions (patterning of phenotypes, non-entropic mutations, compression of genotypes, and natural expression of artistic knowledge) which lead to beneficial results on our problems.

In this paper we propose an evolutionary approach capable of successfully combining rules to play the popular video game, Ms. Pac-Man. In particular we focus our attention on the benefits of using Grammatical Evolution to combine rules in the form of “if condition> then perform action>”. We defined a set of high-level functions that we think are necessary to successfully maneuver Ms. Pac-Man through a maze while trying to get the highest possible score. For comparison purposes, we used four Ms. Pac-Man agents, including a hand-coded agent, and tested them against three different ghosts teams. Our approach shows that the evolved controller achieved the highest score among all the other tested controllers, regardless of the ghost team used.