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Anthony Brabazon - IEEE Xplore Author Profile
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Anthony Brabazon

Also published under: A. Brabazon

Publications
47
Citations
353
Publications by Year
20042021
Co-Authors:
Show All Co-Authors (43)
Image of author Anthony Brabazon

Anthony Brabazon

Also published under: A. Brabazon

Affiliation

Natural Computing Research & Applications Group School of Business, University College Dublin, Ireland

Publication Topics

Biography

Anthony Brabazon received the B.Com. degree from University College Dublin, Dublin, Ireland, in 1988, the first M.S. degree in statistics and the second M.S. degree in operations research from Stanford University, Stanford, CA, USA, in 1994, the M.B.A. degree from Heriot-Watt University, Edinburgh, U.K., in 1998, and the D.B.A. degree from Kingston University, London, U.K., in 2005.,He is currently the Dean of the School of Business, University College Dublin, Dublin, Ireland, where he is the Co-Founder and the Co-Director of the Natural Computing Research and Applications Group. He has published over 200 peer-reviewed studies and authored/edited 16 books. His current research interests include development of natural computing algorithms an...
Publications
47
Citations
353
Publications by Year
20042021
Co-Authors:
Show All Co-Authors (43)
Author's Published Works

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An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures

Michael O’Neill;Anthony Brabazon

2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm. An extremely challenging, yet simple to understand problem landscape is adopted where the probability of randomly finding a solution is approximately one in a trillion. A number of learning mechanisms operating...Show More

An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures

Michael O’Neill;Anthony Brabazon

Year: 2021 | Conference Paper |

Mutational Robustness and Structural Complexity in Grammatical Evolution

Michael O’Neill;Anthony Brabazon

2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
A recent study in Artificial Life found that the need for mutational robustness can give rise to simpler structures in an evolving population. This begs the question, do we observe a similar phenomenon in Genetic Programming? Genetic Programming requires the search of structural space of solutions, usually requiring code growth to find fitter solutions. Typically Genetic Programming algorithms the...Show More

Mutational Robustness and Structural Complexity in Grammatical Evolution

Michael O’Neill;Anthony Brabazon

Year: 2019 | Conference Paper |

How Learning Strategies Can Promote an Evolving Population in Dynamic Environments

Nam Le;Michael O‘Neill;Anthony Brabazon

2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Learning as a form of adaptation has been shown to benefit the evolutionary process through the Baldwin Effect, promoting the adaptivity of an evolving population. Learning generally can be classified into two types: asocial learning, e.g., trial-and-error; and social learning, e.g., imitation learning. Recent research has shown that a learning strategy (or learning rule) - which combines social a...Show More

How Learning Strategies Can Promote an Evolving Population in Dynamic Environments

Nam Le;Michael O‘Neill;Anthony Brabazon

Year: 2019 | Conference Paper |

A Survey of Statistical Machine Learning Elements in Genetic Programming

Alexandros Agapitos;Roisin Loughran;Miguel Nicolau;Simon Lucas;Michael O’Neill;Anthony Brabazon

IEEE Transactions on Evolutionary Computation
Year: 2019 | Volume: 23, Issue: 6 | Journal Article |
Cited by: Papers (21)
Modern genetic programming (GP) operates within the statistical machine learning (SML) framework. In this framework, evolution needs to balance between approximation of an unknown target function on the training data and generalization, which is the ability to predict well on new data. This paper provides a survey and critical discussion of SML methods that enable GP to generalize.Show More

A Survey of Statistical Machine Learning Elements in Genetic Programming

Alexandros Agapitos;Roisin Loughran;Miguel Nicolau;Simon Lucas;Michael O’Neill;Anthony Brabazon

Year: 2019 | Volume: 23, Issue: 6 | Journal Article |

The Baldwin Effect Reconsidered Through the Prism of Social Learning

Nam Le;Michael O'Neill;Anthony Brabazon

2018 IEEE Congress on Evolutionary Computation (CEC)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
Learning, through the Baldwin effect, has showed to successfully guide evolutionary process in a number of research. More interestingly, learning can be classified into two categories. The first one is asocial (individual) learning when learners learn by directly interacting with their environment, e.g. trial-and-error. The other is social learning when learners learn from others, e.g. imitation l...Show More

The Baldwin Effect Reconsidered Through the Prism of Social Learning

Nam Le;Michael O'Neill;Anthony Brabazon

Year: 2018 | Conference Paper |

Foraging inspired algorithms: A design perspective

Anthony Brabazon

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Year: 2017 | Conference Paper |
A notable weakness of the literature concerning foraging inspired algorithms is that little attempt is typically made to rigorously identify the similarities and differences between newly proposed algorithms and existing ones. This has led to a critique from a growing number of researchers that greater efforts need to be made to consolidate the literature on foraging algorithms (and that of metahe...Show More

Foraging inspired algorithms: A design perspective

Anthony Brabazon

Year: 2017 | Conference Paper |

Social Grammatical Evolution with imitation learning for real-valued function estimation

Nam Le;Michael O'Neill;David Fagan;Anthony Brabazon

2017 IEEE Congress on Evolutionary Computation (CEC)
Year: 2017 | Conference Paper |
Drawing on a rich literature concerning social learning in animals, this paper presents a variation of Grammatical Evolution (GE) which incorporates one of the most powerful forms of social learning, namely imitation learning. This replaces the traditional method of `communication' between individuals in GE - crossover - which is drawn from an evolutionary metaphor. The paper provides an introduct...Show More

Social Grammatical Evolution with imitation learning for real-valued function estimation

Nam Le;Michael O'Neill;David Fagan;Anthony Brabazon

Year: 2017 | Conference Paper |

Evolutionary Behavior Tree Approaches for Navigating Platform Games

Miguel Nicolau;Diego Perez-Liebana;Michael O’Neill;Anthony Brabazon

IEEE Transactions on Computational Intelligence and AI in Games
Year: 2017 | Volume: 9, Issue: 3 | Journal Article |
Cited by: Papers (42)
Computer games are highly dynamic environments, where players are faced with a multitude of potentially unseen scenarios. In this paper, AI controllers are applied to the Mario AI benchmark platform, by using the grammatical evolution system to evolve behavior tree structures. These controllers are either evolved to both deal with navigation and reactiveness to elements of the game or used in conj...Show More

Evolutionary Behavior Tree Approaches for Navigating Platform Games

Miguel Nicolau;Diego Perez-Liebana;Michael O’Neill;Anthony Brabazon

Year: 2017 | Volume: 9, Issue: 3 | Journal Article |

An exploration of grammatical encodings to model six nations rugby match outcomes

Michael O'Neill;Anthony Brabazon;David Fagan

2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
We explore the application of grammar-based Genetic Programming, specifically Grammatical Evolution, to the problem of modeling the outcome of Six Nations Rugby matches. A series of grammars are developed in attempts to generate different forms of predictive rules, which might be useful in pre-match and mid-match scenarios. A number of interesting models are generated and their utility discussed.Show More

An exploration of grammatical encodings to model six nations rugby match outcomes

Michael O'Neill;Anthony Brabazon;David Fagan

Year: 2016 | Conference Paper |

Complexity measures in Genetic Programming learning: A brief review

Nam Le;Hoai Nguyen Xuan;Anthony Brabazon;Thuong Pham Thi

2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (22)
Model complexity of Genetic Programming (GP) as a learning machine is currently attracting considerable interest from the research community. Here we provide an up-to-date overview of the research concerning complexity measure techniques in GP learning. The scope of this review includes methods based on information theory techniques, such as the Akaike Information Criterion (AIC), Bayesian Informa...Show More

Complexity measures in Genetic Programming learning: A brief review

Nam Le;Hoai Nguyen Xuan;Anthony Brabazon;Thuong Pham Thi

Year: 2016 | Conference Paper |

Deep evolution of image representations for handwritten digit recognition

Alexandros Agapitos;Michael O'Neill;Miguel Nicolau;David Fagan;Ahmed Kattan;Anthony Brabazon;Kathleen Curran

2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Cited by: Papers (10)
A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single...Show More

Deep evolution of image representations for handwritten digit recognition

Alexandros Agapitos;Michael O'Neill;Miguel Nicolau;David Fagan;Ahmed Kattan;Anthony Brabazon;Kathleen Curran

Year: 2015 | Conference Paper |

Realised volatility forecasting: A genetic programming approach

Zheng Yin;Anthony Brabazon;Conall O'Sullivan;Michael O'Neill

2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
Forecasting daily returns volatility is crucial in finance. Traditionally, volatility is modelled using a time-series of lagged information only, an approach which is in essence a theoretical. Although the relationship of market conditions and volatility has been studied for decades, we still lack a clear theoretical framework to allow us to forecast volatility, despite having many plausible expla...Show More

Realised volatility forecasting: A genetic programming approach

Zheng Yin;Anthony Brabazon;Conall O'Sullivan;Michael O'Neill

Year: 2015 | Conference Paper |

Guidelines for defining benchmark problems in Genetic Programming

Miguel Nicolau;Alexandros Agapitos;Michael O'Neill;Anthony Brabazon

2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Cited by: Papers (18)
The field of Genetic Programming has recently seen a surge of attention to the fact that benchmarking and comparison of approaches is often done in non-standard ways, using poorly designed comparison problems. We raise some issues concerning the design of benchmarks, within the domain of symbolic regression, through experimental evidence. A set of guidelines is provided, aiming towards careful def...Show More

Guidelines for defining benchmark problems in Genetic Programming

Miguel Nicolau;Alexandros Agapitos;Michael O'Neill;Anthony Brabazon

Year: 2015 | Conference Paper |

A genetic programming approach for delta hedging

Zheng Yin;Anthony Brabazon;Conall O'Sullivan;Michael O'Neill

2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Effective hedging of derivative securities is of paramount importance to derivatives investors and to market makers. The standard approach used to hedge derivative instruments is delta hedging. In a Black-Scholes setting, a continuously rebalanced delta hedged portfolio will result in a perfect hedge with no associated hedging error. In reality, continuous rehedging is impossible and this raises t...Show More

A genetic programming approach for delta hedging

Zheng Yin;Anthony Brabazon;Conall O'Sullivan;Michael O'Neill

Year: 2015 | Conference Paper |

An analysis of price impact functions of individual trades on the London Stock Exchange

Mateusz Wilinski;Wei Cui;Anthony Brabazon

2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)
Year: 2014 | Conference Paper |
Studying price impact is important in finance and previous work examines the relationship between trade size and price impact on a number of equity markets. In this study, using recent order book data from the London Stock Exchange, we examine the price impact function for six highly-liquid stocks and novelly investigate whether the function displays time-of-day effects. The results show that pric...Show More

An analysis of price impact functions of individual trades on the London Stock Exchange

Mateusz Wilinski;Wei Cui;Anthony Brabazon

Year: 2014 | Conference Paper |

Ensemble Bayesian Model Averaging in Genetic Programming

Alexandros Agapitos;Michael O'Neill;Anthony Brabazon

2014 IEEE Congress on Evolutionary Computation (CEC)
Year: 2014 | Conference Paper |
Cited by: Papers (6)
This paper considers the general problem of function estimation via Genetic Programming (GP). Data analysts typically select a model from a population of models, and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and lack of generalisation. We adopt a coherent method for accounting for thi...Show More

Ensemble Bayesian Model Averaging in Genetic Programming

Alexandros Agapitos;Michael O'Neill;Anthony Brabazon

Year: 2014 | Conference Paper |

An examination of synchronisation in Artificial Gene Regulatory Networks

Jonathan Byrne;Miguel Nicolau;Anthony Brabazon;Michael O'Neill

2014 IEEE Congress on Evolutionary Computation (CEC)
Year: 2014 | Conference Paper |
Cited by: Papers (2)
An Artificial Genetic Regulatory Network (GRN) is a model of the gene expression regulation mechanism in biological organisms. It is a dynamical system that is capable of mimicking non-linear time series. The GRN was adapted to allow for input and output so that the system's rich dynamics could be used for dynamic problem solving. In order for the GRN to be embedded in the environment, the time sc...Show More

An examination of synchronisation in Artificial Gene Regulatory Networks

Jonathan Byrne;Miguel Nicolau;Anthony Brabazon;Michael O'Neill

Year: 2014 | Conference Paper |

An agent-based modeling approach to study price impact

Wei Cui;Anthony Brabazon

2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)
Year: 2012 | Conference Paper |
Cited by: Papers (16)
Price impact models are important for devising trade execution strategies. However, a proper characterization of price impacts is still lacking. This study models the price impact using an agent-based modeling approach. The purpose of this paper is to investigate whether agent intelligence is a necessary condition when seeking to construct realistic price impact with an artificial market simulatio...Show More

An agent-based modeling approach to study price impact

Wei Cui;Anthony Brabazon

Year: 2012 | Conference Paper |

Termination in Grammatical Evolution: grammar design, wrapping, and tails

Miguel Nicolau;Michael O'Neill;Anthony Brabazon

2012 IEEE Congress on Evolutionary Computation
Year: 2012 | Conference Paper |
Cited by: Papers (13)
This paper explores the issues with mapping termination in Grammatical Evolution, and examines approaches that can be used to minimise them. It analyses the traditional approach of reusing the same genetic material, known as wrapping, and shows why this is inefficient with some grammars used in the literature. It suggests the appending of non-coding genetic material to genotype strings, at the sta...Show More

Termination in Grammatical Evolution: grammar design, wrapping, and tails

Miguel Nicolau;Michael O'Neill;Anthony Brabazon

Year: 2012 | Conference Paper |

Optimal patent design: An agent-based approach

Anthony Brabazon;Arlindo Silva;Michael O'Neill

2012 IEEE Congress on Evolutionary Computation
Year: 2012 | Conference Paper |
Although significant attention is given to the study of intellectual property rights (IPR) in economic and other literatures our understanding of the impact of these rights on the process of technological advance is surprisingly incomplete. In this paper we focus on one form of IPR, namely patents. An important and open question faced by policy-makers is what form of patent regime will encourage t...Show More

Optimal patent design: An agent-based approach

Anthony Brabazon;Arlindo Silva;Michael O'Neill

Year: 2012 | Conference Paper |

Reactiveness and navigation in computer games: Different needs, different approaches

Diego Perez;Miguel Nicolau;Michael O'Neill;Anthony Brabazon

2011 IEEE Conference on Computational Intelligence and Games (CIG'11)
Year: 2011 | Conference Paper |
Cited by: Papers (7)
This paper presents an approach to the Mario AI Benchmark problem, using the A* algorithm for navigation, and an evolutionary process combining routines for the reactiveness of the resulting bot. The Grammatical Evolution system was used to evolve Behaviour Trees, combining both types of routines, while the highly dynamic nature of the environment required specific approaches to deal with over-fit...Show More

Reactiveness and navigation in computer games: Different needs, different approaches

Diego Perez;Miguel Nicolau;Michael O'Neill;Anthony Brabazon

Year: 2011 | Conference Paper |

Learning environment models in car racing using stateful Genetic Programming

Alexandros Agapitos;Michael O'Neill;Anthony Brabazon;Theodoros Theodoridis

2011 IEEE Conference on Computational Intelligence and Games (CIG'11)
Year: 2011 | Conference Paper |
Cited by: Papers (7)
For computational intelligence to be useful in creating game agent AI we need to focus on methods that allow the creation and maintenance of models for the environment, which the artificial agents inhabit. Maintaining a model allows an agent to plan its actions more effectively by combining immediate sensory information along with a memories that have been acquired while operating in that environm...Show More

Learning environment models in car racing using stateful Genetic Programming

Alexandros Agapitos;Michael O'Neill;Anthony Brabazon;Theodoros Theodoridis

Year: 2011 | Conference Paper |

Higher-order functions in aesthetic EC encodings

James McDermott;Jonathan Byrne;John Mark Swafford;Michael O'Neill;Anthony Brabazon

IEEE Congress on Evolutionary Computation
Year: 2010 | Conference Paper |
Cited by: Papers (4)
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 ...Show More

Higher-order functions in aesthetic EC encodings

James McDermott;Jonathan Byrne;John Mark Swafford;Michael O'Neill;Anthony Brabazon

Year: 2010 | Conference Paper |

Defining locality in genetic programming to predict performance

Edgar Galván-López;James McDermott;Michael O'Neill;Anthony Brabazon

IEEE Congress on Evolutionary Computation
Year: 2010 | Conference Paper |
Cited by: Papers (14)
A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype-fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover ...Show More

Defining locality in genetic programming to predict performance

Edgar Galván-López;James McDermott;Michael O'Neill;Anthony Brabazon

Year: 2010 | Conference Paper |

Evolving efficient limit order strategy using Grammatical Evolution

Wei Cui;Anthony Brabazon;Michael O'Neill

IEEE Congress on Evolutionary Computation
Year: 2010 | Conference Paper |
Cited by: Papers (2)
Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. A practical problem in trade execution is how to trade a large order as efficiently as possible. A trade execution strategy is designed for this task to minimize total trade cost. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which ...Show More

Evolving efficient limit order strategy using Grammatical Evolution

Wei Cui;Anthony Brabazon;Michael O'Neill

Year: 2010 | Conference Paper |

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