Michael Fenton

Combining structural analysis and multi-objective criteria for evolutionary architectural design

This study evolves and categorises a population of conceptual designs by their ability to handle physical constraints. The design process involves a trade-off between form and function. The aesthetic considerations of the designer are constrained by physical considerations and material cost. In previous work, we developed a design grammar capable of evolving aesthetically pleasing designs through the use of an interactive evolutionary algorithm. This work implements a fitness function capable of applying engineering objectives to automatically evaluate designs and, in turn, reduce the search space that is presented to the user.

String-Rewriting Grammars for Evolutionary Architectural Design

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: Stinys shape grammars. The advantages and disadvantages of the two types of grammars for design tasks are discussed.

PonyGE2: grammatical evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCDs Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.

Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution

The majority of existing discrete truss optimization methods focus primarily on optimizing global truss topology using a ground structure approach, in which all possible node and beam locations are specified a priori. The ground structure discrete optimization method has been shown to be restrictive as it limits derivable solutions to what is explicitly defined. Greater representational freedom can improve performance. In this paper, grammatical evolution is applied. It can represent a variable number of nodes and their locations on a continuum. A novel method of connecting evolved nodes using a Delaunay triangulation algorithm shows that fully triangulated, kinematically stable structures can be generated. Discrete beam-truss structures can be optimized without the need for any information about the desired form of the solution other than the design envelope. Our technique is compared to existing discrete optimization techniques, and notable savings in structure self-weight are demonstrated. In particular, our new method can produce results superior to those reported in the literature in cases in which the problem is ill-defined and the structure of the solution is not known a priori.

Managing Repetition in Grammar-Based Genetic Programming

Grammar-based Genetic Programming systems are capable of generating identical phenotypic solutions, either by creating repeated genotypic representations, or from distinct genotypes, through their many-to-one mapping process. Furthermore, their initialisation process can generate a high number of duplicate individuals, while traditional variation and replacement operators can permit multiple individuals to percolate through generations unchanged. This can lead to a high number of phenotypically identical individuals within a population. This study investigates the frequency and effect of such duplicate individuals on a suite of benchmark problems. Both Grammatical Evolution and the CFG-GP systems are examined. Experimental evidence suggests that these useless evaluations can be instead be used either to speed-up the evolutionary process, or to delay convergence.

Exploring position independent initialisation in grammatical evolution

Initialisation in Grammatical Evolution (GE) is a topic that remains open to debate on many fronts. The literature falls between two mainstay approaches: random and sensible initialisation. These methods are not without their drawbacks with the type of trees generated. This paper tackles this problem by extending these traditional operators to incorporate position independence in the initialisation process in GE. This new approach to initialisation is shown to provide a viable alternative to the commonly used approaches, whilst avoiding the common pitfalls of traditional approaches to initialisation.

Optimising complex pylon structures with grammatical evolution

Evolutionary algorithms have proven their ability to optimise architectural designs but are limited by their representation, i.e., the structures that the algorithm is capable of generating. The representation is normally constrained to small structures, or parts of a larger structure, to prevent a preponderance of invalid designs. This work uses a grammar based representation to generate large scale pylon designs. It removes invalid designs from the search space, but still allows complex and large scale constructions. In order to show the suitability of this method to real world design problems, we apply it to the Royal Institute of British Architects pylon design competition. This work shows that a combination of a grammar representation with real world constraints is capable of exploring different design configurations while evolving viable and optimised designs.

A search for improved performance in regular expressions

The primary aim of automated performance improvement is to reduce the running time of programs while maintaining (or improving on) functionality. In this paper, Genetic Programming is used to find performance improvements in regular expressions for an array of target programs, representing the first application of automated software improvement for run-time performance in the Regular Expression language. This particular problem is interesting as there may be many possible alternative regular expressions which perform the same task while exhibiting subtle differences in performance. A benchmark suite of candidate regular expressions is proposed for improvement. We show that the application of Genetic Programming techniques can result in performance improvements in all cases. As we start evolution from a known good regular expression, diversity is critical in escaping the local optima of the seed expression. In order to understand diversity during evolution we compare an initial population consisting of only seed programs with a population initialised using a combination of a single seed individual with individuals generated using PI Grow and Ramped-half-and-half initialisation mechanisms.

Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming

Heterogeneous Cellular Networks are multi-tiered cellular networks comprised of Macro Cells and Small Cells in which all cells occupy the same bandwidth. User Equipments greedily attach to whichever cell provides the best signal strength. While Macro Cells are invariant, the power and selection bias for each Small Cell can be increased or decreased (subject to pre-defined limits) such that more or fewer UEs attach to that cell. Setting optimal power and selection bias levels for Small Cells is key for good network performance. The application of Genetic Programming techniques has been proven to produce good results in the control of Heterogenous Networks. Expanding on previous works, this paper uses grammatical GP to evolve distributed control functions for Small Cells in order to vary their power and bias settings. The objective of these control functions is to evolve control functions that maximise a proportional fair utility of UE throughputs.

Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming

Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.