Robert I. McKay

Australian National University | ANU · Research School of Computer Science

Skeleton sequences are lightweight and compact and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3-D joint coordinates as spatial–temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance. The use...

Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a...

Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a...

Skeleton sequences are light-weight and compact, and thus ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion, to boost recognition performance. The use o...

Modeling real-world phenomena is a focus of many science and engineering efforts, from ecological modeling to financial forecasting. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Knowledge-driven modeling builds a model based on human expertise, yet is oft...

Modeling real-world phenomena is a focus of many science and engineering efforts, such as ecological modeling and financial forecasting, to name a few. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Towards this goal, knowledge-driven modeling builds a mode...

Although no consensus has been reached on the conditions under which modularity emerges, we lind the idea of specialisation driving modularity highly plausible. Current abstractions have demonstrated emergence of modularity in scenarios evolving Gene Regulatory Networks to show simple targeted behaviours, but these methods are less successful in mo...

Raven's Progressive Matrices have been widely used for measuring abstract reasoning and intelligence in humans. However for artificial learning systems, abstract reasoning remains a challenging problem. In this paper we investigate how neural networks augmented with biologically inspired spiking modules gain a significant advantage in solving this...

This paper uses distributional analysis to disentangle the effects of fitness landscape and stochasticity in Wagner's Gene Regulatory Network Based evolutionary scenario. Further advantages of the analysis include the ability to accurately identify global optima and potential extensions to approximate the distributional fitness more accurately than...

This paper uses distributional analysis to disentangle the effects of fitness landscape and stochasticity in Wagner's Gene Regulatory Network Based evolutionary scenario. Further advantages of the analysis include the ability to accurately identify global optima and potential extensions to approximate the distributional fitness more accurately than...

This paper uses distributional analysis to disentangle the effects of fitness landscape and stochasticity in Wagner's Gene Regulatory Network Based evolutionary scenario. Further advantages of the analysis include the ability to accurately identify global optima and potential extensions to approximate the distributional fitness more accurately than...

Espinosa-Soto and Wagner (2010) introduced a domain with weak assumptions on biology and environment, where modular structures emerge under simple evolutionary processes. We found a number of anomalous behaviours: modularity emerged in this domain, but could not dominate populations as observed in biology. Highly fit, modular solutions exist in th...

Wagner's modularity inducing problem domain is a key contribution to the study of the evolution of modularity, including both evolutionary theory and evolutionary computation. We study its behavior under classical genetic algorithms. Unlike what we seem to observe in nature, the emergence of modularity is highly conditional and dependent, for examp...

We explore the problem space of maximum nonlinearity problems for balanced Boolean functions, examining the symmetry structure and fitness landscapes in the most common (bit string) representation. We present theoretical analyses of well understood aspects, together with detailed enumeration of the 4-bit problem, sampling of the 6-bit problem based...

We propose an efficient low-overhead methodology for screening key layouts for ultimate typing speed. It is fast and complementary to existing protocols in other ways. For equal overhead, it allows testing over a wider range of users. It is subject to potential biases, but they can be quantified and adjusted. It assumes an existing standard layout...

We predict the progression of Immunoglobulin A Nephropathy using three classification methods: Classification and Regression Trees, Logistic Regression, and Feed-Forward Artificial Neural Networks. We treat it as a classification problem, of predicting progression to end-stage renal disease in the ten years following initial diagnosis. We compared...

Abstract—Estimation of distribution algorithms applied to genetic programming have been studied by a number of authors. Like all estimation of distribution algorithms, they suffer from biases induced by the model building and sampling process. However, the biases are amplified in the algorithms for genetic programming. In particular, many systems u...

We predict the progression of Immunoglobulin A Nephropathy using three of the most widely used supervised classification machine learning algorithms: Classification and Regression Trees, Logistic Regression (in two different forms), and Feed-Forward Neural Networks. The problem is treated as a classification problem, of predicting progression to en...

We suggest a novel memory-based metaheuristic optimization algorithm, VLR, which uses a list of already-visited areas to more effectively search for an optimal solution. We chose the Max-cut problem to test its optimization performance, comparing it with state-of-the-art methods.VLRdominates the previous best-performing heuristics.We also undertake...

Probabilistic model-building algorithms (PMBA), a subset of evolutionary algorithms, have been successful in solving complex problems, in addition providing analytical information about the distribution of fit individuals. Most PMBA work has concentrated on the string representation used in typical genetic algorithms. A smaller body of work has aim...

Watson and Lovelock’s daisyworld is a coupled biotic–abiotic feedback loop exhibiting interesting planetary ecodynamics. Previous studies have shown fascinating spatio-temporal dynamics in a 2D daisyworld, with the emergence of complex spatial patterns. We introduce small-world effect into such a system. Even a small fraction of long-range coupling...

Locality has long been seen as a crucial property for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. A number of studies investigating the effects of locality in GP can be found in the literature. The majority of the previous research on locality focuses on syntactic aspects, and operator semantic...

Genetic programming is very computationally intensive, particularly in CPU time. A number of approaches to evaluation cost reduction have been proposed, among them early termination of evaluation (applicable in problem domains where estimates of the final fitness value are available during evaluation). Like all cost reduction techniques, early term...

Symmetry has hitherto been studied piecemeal in a variety of evolutionary computation domains, with little consistency between the definitions. Here we provide formal definitions of symmetry that are consistent across the field of evolutionary computation. We propose a number of evolutionary and estimation of distribution algorithms suitable for va...

We investigate the impact of early stopping on the speed and accuracy of Genetic Programming (GP) learning from noisy data. Early stopping, using a popular stopping criterion, maintains the generalisation capacity of GP while significantly reducing its training time.

In the regulated Nakdong River, algal proliferations are annually observed in some seasons, with cyanobacteria (Microcystis aeruginosa) appearing in summer and diatom blooms (Stephanodiscus hantzschii) in winter. This study aims to develop two ecological models forecasting future chlorophyll a at two time-steps (one-week and one-year forecasts), us...

We present a rapid screening method for keyboard layouts. The method relies on user familiarity with a pre-existing layout, and can provide rapid, low-cost estimates of the eventual user speed on the new layout. It provides an effective pre-screening for the expensive training curve estimates of previous methods, and provides valuable supplementary...

Some Genetic Programming (GP) systems have fewer structural constraints than expression tree GP, permitting a wider range of operators. Using one such system, TAG3P, we compared the effects of such new operators with more standard ones on individual fitness, size and depth, comparing them on a number of symbolic regression and tree structuring prob...

This work introduces hardware implementation of artificial neural networks (ANNs) with learning ability on field programmable gate array (FPGA) for dynamic system identification. The learning phase is accomplished by using the improved particle swarm ...

We introduce replicator-mutator mechanisms from evolu-tionary dynamics into a two-dimensional daisyworld model, thereby coupling evolutionary changes with daisyworld's bi-directional feedback between biota and environment. Daisy-world continues to self-regulate in the presence of these evo-lutionary forces. The most interesting behaviours, exhibit-...

The Gaussian Q-function is the integral of the tail of the Gaussian distribution; as such, it is important across a vast range of fields requiring stochastic analysis. No elementary closed form is possible, so a number of approximations have been proposed. We use a Genetic Programming (GP) system, Tree Adjoining Grammar Guided GP (TAG3P) with local...

Nature has always been a source of inspiration. Over the last few decades, it has stimulated many suc- cessful techniques, algorithms and computational applications for dealing with large, complex and dynamic real world problems. In this chapter, the authors discuss why nature-inspired solutions have become increasingly important and favourable for...

Much recent research in Estimation of Distribution Algorithms (EDA) applied to Genetic Programming has adopted a Stochastic Context Free Grammar(SCFG)-based model formalism. However these methods generate biases which may be indistinguishable from selection bias, resulting in sub-optimal performance. The primary factor generating this bias is the c...

The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterized by the Gaussian distribution. However, no simple exact closed form of the Q-function is known. Consequently, a number of approximations have been proposed over the past several decades. In this paper, we use Genetic Programming with...

Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful techniques, algorithms and computational applications for dealing with large, complex and dynamic real world problems. In this article, the authors discuss why nature-inspired solutions have become increasingly important and favourable for t...

Operator adaptation in evolutionary computation has previously been applied to either small numbers of operators, or larger numbers of fairly similar ones. This paper focuses on adaptation in algorithms offering a diverse range of operators. We compare a number of previously-developed adaptation strategies, together with two that have been specific...

Watson and Lovelock's daisyworld model [1] was devised to demon-strate how the biota of a world could stabilise it, driving it to a temperature regime that favoured survival of the biota. The subsequent studies have focused on the be-haviour of daisyworld in various fields. This study looks at the emergent patterns that arise in 2D daisyworlds at d...

In estimation of distribution algorithms (EDAs), probability models hold accumulating evidence on the location of an optimum. Stochastic sampling drift has been heavily researched in EDA optimization but not in EDAs applied to genetic programming (EDA-GP). We show that, for EDA-GPs using probabilistic prototype tree models, stochastic drift in samp...

Directed protein evolution has led to major advances in organic chemistry, enabling the development of highly optimised proteins. The SELEX method has also been highly effective in evolving ribose nucleic acid (RNA) or deoxy-ribose nucleic acid (DNA) molecules; variants have been proposed which allow SELEX to be used in protein evolution. All of th...

Current layouts for alphabetic input on mobile phone keypads are inefficient. We propose a genetic algorithm (GA) to find a suitable keypad layout for each user, based on their personal text history. It incorporates codes for frequent multigrams. We optimize for two-thumb use, minimizing the number of strokes, and consecutive use of the same key or...

Estimation of Distribution Algorithms were introduced into Genetic Programming over 15 years ago, and have demonstrated good performance on a range of problems, but there has been little research into their limitations. We apply two such algorithms - scalar and vectorial Stochastic Grammar GP - to Daida's well-known Lid problem, to better understan...

All evolutionary algorithms trade off exploration and exploitation in optimisation problems; dynamic problems are no exception. We investigate this trade-off, over a range of algorithm settings, on dynamic variants of three well-known optimisation problems (One Max, Royal Road and knapsack), using Yang's XOR method to vary the scale and rate of cha...

We investigate interactions between evolution, development and lifelong layered learning in a combination we call evolutionary developmental evaluation (EDE), using a specific implementation, developmental tree-adjoining grammar guided genetic programming (GP). The approach is consistent with the process of biological evolution and development in h...

Chemical methods such as directed evolution and some forms of the SELEX procedure implement evolutionary algorithms directly in vitro. They have a wide range of applications in detecting and targeting diseases and potential applications in other areas as well [1]. However it is relatively difficult and expensive to carry out these processes (by com...

In this paper, we investigate the impact of a lay- ered learning approach with incremental sampling on Genetic Programming (GP). The new system, called GPLL, is tested and compared with standard GP on twelve symbolic regression problems. While GPLL does not differ from standard GP on univariate target functions, it has better training efficiency on...

We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operato...

We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that...

In a complex crime scene with many possible suspects and conflicting evidence,crime investigation requires scientific and logical steps to narrow down the suspects. Since human investigators have difficulty in fully handling all reasoning in this highly comp lex hypothesis space, we propose a decision support system to aid the investigation process...

Daisyworld was initially proposed as an abstract model of the self-regulation of planetary ecosystems. The original one-point model has also been extended to one- and two-dimensional worlds. The latter are especially interesting, in that they not only demonstrate the emergence of spatially-stabilised homeostasis but also emphasise dynamics of heter...

Analysis of artificial evolutionary systems uses post-processing to extract information from runs. Many effective methods have been developed, but format incompatibilities limit their adoption. We propose a solution combining XML and compression, which imposes modest overhead. We describe the steps to integrate our schema in existing systems and to...

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://cre-ativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Ecological modeling faces some unique probl...

This paper investigates the role of syntactic locality and semantic locality of crossover in Genetic Programming (GP). First we propose a novel crossover using syntactic locality, Syntactic Similarity based Crossover (SySC). We test this crossover on a number of real-valued symbolic regression problems. A comparison is undertaken with Standard Cros...

Welcome to genetic programming, where the forces of nature are used to automatically evolve computer programs. We give a flavour of where GP has been successfully applied (it is far too wide an area to cover everything) and interesting current and future research but start with a tutorial of how to get started and finish with common pitfalls to avo...

Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and...

Probabilistic models are widely used in evolutionary and related algorithms. In Genetic Programming (GP), the Probabilistic Prototype Tree (PPT) is often used as a model representation. Drift due to sampling bias is a widely recognised problem, and may be serious, particularly in dependent probability models. While this has been closely studied in...

This paper investigates the role of semantic diversity and locality of crossover operators in. Genetic Programming (GP) for Boolean problems. We propose methods for measuring and storing semantics of subtrees in Boolean domains using Trace Semantics, and design several new crossovers on this basis. They can be categorised into two classes depending...

The main focus of this paper is to propose integration of dynamic and multiobjective algorithms for graph clustering in dynamic environments under multiple objectives. The primary application is to multiobjective clustering in social networks which change over time. Social networks, typically represented by graphs, contain information about the rel...