Colin Johnson

Colin Johnson
University of Nottingham | Notts · School of Computer Science

BA, MPhil, PhD

About

152
Publications
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Publications

Publications (152)
Article
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Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and co...
Chapter
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This paper describes an ongoing project to create a “digital mirror” to my practice as a composer of contemporary classical music; that is, a system that takes descriptions (in code) of aspects of that practice, and reflects them back as computer-generated realisations. The paper describes the design process of this system, explains how it is imple...
Article
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This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a set of sample solutions to the problem. These functions are learned using a supervised learning approa...
Chapter
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Software development is a complex activity requiring intelligent action. This paper explores the use of an AI technique for one step in software development, viz. detecting the location of a fault in a program. A measure of program progress is proposed, which uses a Naïve Bayes model to measure how useful the information that has been produced by t...
Preprint
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Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and co...
Conference Paper
This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6% for the EMG model, and 3.6% when accelerometry data is i...
Article
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Can autonomous systems be musically creative without musical knowledge? Assumptions from interdisciplinary studies on self-reflection are evaluated using Video Interactive VST Orchestra, a system that generates music from audio and video inputs through an analysis of video motion and simultaneous sound processing. The system is able to generate mat...
Chapter
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This paper explores how Learned Guidance Functions (LGFs)—a pre-training method used to smooth search landscapes—can be used as a fitness function for evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscram...
Article
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One of the general aims of evolutionary art research is to build a computer system capable of creating interesting, beautiful, or creative results, including images, videos, animations, text, and performances. In this context, it is crucial to understand how fitness is conceived and implemented to explore the “interestingness,” beauty, or creativit...
Chapter
In this paper, we address the stagnation of RoboCup competitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of...
Conference Paper
The essential rationale for subtype polymorphism is adherence to the 'Open/Closed Principle' [12]: the ability to write framework code in terms of superclasses and subsequently invoke it with any subclass that exhibits 'proper subtyping' via the Liskov Substitution Principle (LSP) [11]. Formally, the LSP states that if ø(t : T) is a provable proper...
Article
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Interactive Music Systems (IMS) have introduced a new world of music-making modalities. But can we really say that they create music, as in true autonomous creation? Here we discuss Video Interactive VST Orchestra (VIVO), an IMS that considers extra-musical information by adopting a simple salience based model of user-system interaction when simula...
Conference Paper
The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. An alternative to this is event-based summaries. Directional changes (DC), which is a new event-based summary method, allows for new regularities in data to be discovered and exploited,...
Conference Paper
In this paper, we discuss students' expectations and experiences in the first term of the Year in Computing, a new programme for non-computing majors at the University of Kent, a public research university in the UK. We focus on the effect of students' home discipline on their experiences in the programme and situate this work within the context of...
Article
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This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is...
Conference Paper
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In the path planning problem for autonomous mobile robots, robots have to plan their path from the start position to the goal. In this paper, we investigate the application of the MMAS algorithm to the exploratory path planning problem, in which the robots should explore the environment at the same time they plan the path. Max-min ant system is an...
Conference Paper
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Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improving them. These methods...
Conference Paper
Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with exi...
Conference Paper
Have you ever noticed that your car never achieves the fuel economy claimed by the manufacturer? Does this seem unfair? Unscientic? Would you like the same situation to occur in Genetic Improvement? Comparison will always be difficult [9], however, guidelines have been discussed [3, 5, 4]. With two GP [8] approaches, comparing the number of evaluat...
Article
Full-text available
This paper is concerned with the idea of fitness in art and music systems that are based on evolutionary computation. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. A large collection of papers are classif...
Conference Paper
This work proposes a theoretical architectural model based on the brain's fear learning system with the purpose of generating artificial fear conditioning at both stimuli and context abstraction levels in robot companions. The proposed architecture is inspired by the different brain regions involved in fear learning, here divided into four modules...
Conference Paper
"If you cannot measure it, you cannot improve it. Lord Kelvin Fitness in GP/GI is usually a short-sighted greedy fitness function counting the number of satisfied test cases (or some other score based on error). If GP/GI is to be extended to successfully tackle "full software systems", which is the stated domain of Genetic Improvement, with loops,...
Conference Paper
In this paper, we investigate the notion that there may be alternate methods, beyond typical rectilinear interpolations such as Bilinear Interpolation, that have a greater suitability for use in visual/image preprocessors for Artificial Neural Networks. We present a novel method for down-sampling image data in preparation for a Feed-Forward Percept...
Conference Paper
We present an artificial synaptic plasticity (ASP) mechanism that allows artificial systems to make associations between environmental stimuli and learn new skills at runtime. ASP builds on the classical neural network for simulating associative learning, which is induced through a conditioning-like procedure. Experiments in a simulated mobile robo...
Article
Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical si...
Conference Paper
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There are many different genetic programming (GP) frameworks that can be used to implement algorithms to solve a particular optimization problem. In order to use a framework, users need to become familiar with a large numbers of source code before actually implementing the algorithm, adding a learning overhead. In some cases, this can prevent users...
Article
This article explores the idea of Internet search as a technology to underpin artistic creation. Concepts of interactivity in art and music are explored, and then an overview of different types of Internet-based art is presented. A number of different ways in which Internet search have the potential to underpin artistic and musical activity are the...
Conference Paper
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EpochX is a genetic programming framework with provision for event management - similar to the Java event model - allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX geneti...
Conference Paper
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Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems. The main motivation is to allow GP to deal with more complex problems. Most previous works on modularity in GP emphasise the structure of mo...
Article
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This paper explores the problem of unknotting closed braids and classical knots in mathematical knot theory. We apply evolutionary computation methods to learn sequences of moves that simplify knot diagrams, and show that this can be effective both when the evolution is carried out for individual knots and when a generic sequence of moves is evolve...
Article
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Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not copin...
Article
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Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel...
Conference Paper
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Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation (e.g., parse trees), regardless of their semantic. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view...
Article
Computational creativity is the application of computers to perform tasks that would be regarded as creative if performed by humans. One approach to computational creativity is to regard it as a search process, where some conceptual space is searched, and perhaps transformed, to find an outcome that would be regarded as creative. Typically, such se...
Conference Paper
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EpochX is a Genetic Programming (GP) framework written in Java. It allows the creation of tree-based and grammar-based GP systems. It has been created to reflect typical ways in which Java programmers work, for example, borrowing from the Java event model and taking inspiration from the Java collections framework. This paper presents EpochX in gene...
Conference Paper
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Genetic programming has proven capable of evolving solutions to a wide variety of problems. However, the successes have largely been with programs without iteration or recursion; evolving recursive programs has turned out to be particularly challenging. The main obstacle to evolving recursive programs seems to be that they are particularly fragile...
Conference Paper
Full-text available
Variables are a fundamental component of computer programs. However, rarely has the construction of new variables been left to the evolutionary process of a tree-based Genetic Programming system. We present a series of modifications to an existing GP approach to allow the evolution of high-level imperative programs with limited scope variables. We...
Conference Paper
This paper explores the idea of applying evolutionary algorithms to those search spaces that are defined extensionally, i.e. by listing every item in the space. When these spaces are with a function that returns similar elements given a key element, analogies of mutation and crossover can be defined. This idea is discussed in general, and specific...
Conference Paper
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We present a set of extensions to Montana's popular Strongly Typed Genetic Programming system that introduce constraints on the structure of program trees. It is demonstrated that these constraints can be used to evolve programs with a naturally imperative structure, us-ing common high-level imperative language constructs such as loops. A set of th...
Conference Paper
This paper considers the notion of fitness in evolutionary art and music. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. Papers from a large collection are classified using this taxonomy. The paper then di...
Article
Full-text available
This paper demonstrates how association rule mining can be applied to discover relations between two ontologies of folk music: a genre and a region ontology. Genre– region associations have been widely studied in folk mu-sic research but have been neglected in music information retrieval. We present a method of association rule min-ing with constra...
Article
Twelve years have passed since the advent of grammatical evolution (GE) in 1998, but such issues as vast search space, genotypic readability, and the inherent relationship among grammatical concepts, production rules and derivations have remained untouched in almost all existing GE researches. Model-based approach is an attractive method to achieve...
Article
Almost all existing genetic programming systems deal with fitness evaluation solely by testing. In this paper, by contrast, we present an original approach that combines genetic programming with Hoare logic with the aid of model checking and finite state automata, henceby proposing a brand new verification-focused formal genetic programming system...
Article
This paper is concerned with the application of ideas inspired by developmental biology to the evolution of cellular automata rules using genetic programming. In particular, it is focused on so-called self-assembling patterns. The application of development in computing is reviewed, as is the evolutionary technique used in the paper-Cartesian Genet...
Conference Paper
Full-text available
This paper presents a framework for heuristic portfolio optimisation applied to a hedge fund investment strategy. The first contribution of the paper is to present a framework for implementing portfolio optimisation of a market neutral hedge fund strategy. The paper also illustrates the application of the recently developed Geometric Nelder-Mead Al...
Conference Paper
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Ant Colony Optimization (ACO) differs substantially from other meta-heuristics such as Evolutionary Algorithms (EA). Two of its distinctive features are: (i) it is constructive rather than based on iterative improvements, and (ii) it employs problem knowledge in the construction process via the heuristic function, which is essential for its success...
Article
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This paper proposes a novel ant colony optimisation (ACO) algorithm tailored for the hierarchical multi-label classification problem of protein function prediction. This problem is a very active research field, given the large increase in the number of uncharacterised proteins available for analysis and the importance of determining their functions...
Conference Paper
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Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string. We show that this leads to a large increase in efficiency compared with standard C...
Conference Paper
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This paper investigates the effect of different levels of rationality on the Lucas-Islands model of economic behaviour. In particular, this is studied through the use of Agent-based Computational Economics, where individual economic agents are represented by separate computational entities in an interacting computer simulation. Three different econ...
Conference Paper
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The Nelder-Mead Algorithm (NMA) is an almost half-century old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are recently introduced formal generalization of traditional PSO and...
Conference Paper
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An often-mentioned issue with Grammatical Evolution is that a small change in the genotype, through mutation or crossover, may completely change the meaning of all of the following genes. This paper analyses the crossover and mutation operations in GE, in particular examining the constructive or destructive nature of these operations when occurring...
Article
Fimbriae are structures in Escherichia coli, the expression of which is controlled by the fim operon. Understanding this expression is important because the fimbriae are important virulence factors. This expression can be studied using targeted mutations to the DNA, which can be used to disable binding or transcription of a protein. However, this...
Article
Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populat...
Article
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The levels of expression of the four receptors and eleven ligands composing the epidermal growth factor family were measured using immunohistochemical staining in one hundred cases of breast cancer. All of the family were expressed to some degree in some cases; however, individual cases showed a very wide range of expression of the family from esse...
Conference Paper
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This paper presents an agent-based computational economics model (ACE) to study demand-pull and cost-push inflation. Moreover, it studies the effect of different levels of rationality on the equilibrium price and unemployment rate. The model examines three different economies. In the first economy workers choose firms randomly, in the second econom...
Conference Paper
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Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark geneti...
Conference Paper
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This paper proposes a novel Ant Colony Optimisation algorithm for the hierarchical problem of predicting protein functions using the Gene Ontology (GO). The GO structure represents a challenging case of hierarchical classification, since its terms are organised in a direct acyclic graph fashion where a term can have more than one parent — in contra...
Conference Paper
One justification for the use of crossover operators in Genetic Programming is that the crossover of program syntax gives rise to the crossover of information at the semantic level. In particular, a fitness-increasing crossover is presumed to act by combining fitness-contributing components of both parents. In this paper we investigate a particular...
Conference Paper
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Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of ant colony optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extending the approach for coping with continuous attributes presente...
Article
This paper consists of a discussion of the potential impact on computer science education of regarding computation as a property of the natural world, rather than just a property of artifacts specifically created for the purpose of computing. Such a perspective is becoming increasingly important: new computing paradigms based on the natural computa...
Chapter
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Many classification schemes for defining protein functions, such as Gene Ontology (GO), are organised in a hierarchical structure. Nodes near the root of the hierarchy represent general functions while nodes near the leaves of the hierarchy represent more specific functions, giving the flexibility to specify at which level the protein will be annot...
Chapter
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We propose a new hybrid data mining method for predicting protein-protein interactions combining Likelihood-Ratio with rule induction algorithms. In essence, the new method consists of using a rule induction algorithm to discover rules representing partitions of the data, and then the discovered rules are interpreted as “bins” which are used to com...
Article
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The aim of this paper is to discuss our experience with, and some broader thoughts on, the use of student-produced podcasts as a means of supporting and assessing learning. The results of an assessment using this medium are reported, and student evaluation of the assessment presented and discussed.
Conference Paper
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This paper presents an extension to Ant-Miner, named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes “on-the-fly”, cAnt-Min...
Article
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Cellular processes often hinge upon specific interactions among proteins, and knowledge of these processes at a system level constitutes a major goal of proteomics. In particular, a greater understanding of protein-protein interactions can be gained via a more detailed investigation of the protein domain interactions that mediate the interactions o...
Conference Paper
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We review progress in Grand Challenge 7 : Journeys in Non-Classical Computation. We overview GC7- related events, review some background work in certain aspects of GC7 (hypercomputation, bio- inspired computation, and embodied computation) and identify some of the unifying challenges. We review the progress in implementations of one class of non-cl...
Chapter
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This chapter gives an overview of computational models and simulations of the EGF receptor system. It begins with a survey of motivations for producing such models and then describes the main approaches that are taken to carrying out such modeling, with respect to differential equations and individual-based modeling. Finally, a number of projects t...
Conference Paper
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This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is explored. Two experiments using a fitness - adaptive walk algorithm are carried out: the first is concerned with function optimization with random Boolean networks, the second with...
Conference Paper
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Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better...
Article
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This paper is concerned with taking an engineering approach towards the application of metaheuristic problem solving methods, i.e., heuristics that aim to solve a wide variety of problems. How can a practitioner solve a problem using metaheuristic methods? What choices do they have, and how are these choices influenced by the problem at hand? Are t...
Article
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The discrete particle swarm optimization (DPSO) algorithm is an optimization technique which belongs to the fertile paradigm of Swarm Intelligence. Designed for the task of attribute selection, the DPSO deals with discrete variables in a straightforward manner. This work empowers the DPSO algorithm by extending it in two ways. First, it enables the...