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An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures
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
Localisation in Wireless Networks using Deep Bidirectional Recurrent Neural Networks
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
The accurate localisation and tracking of objects is crucial in many domains. In this paper, we focus on location tracking in wireless networks. Reliable localisation will be essential for self-driving, future factories, and beamforming in 5G deployments. Time-of-arrival (TOA) based localisation systems use synchronised nodes to receive radio signals sent by transmitters at the object to be locate...Show More
Hierarchical Grammar-Guided Genetic Programming Techniques for Scheduling in Heterogeneous Networks
Takfarinas Saber;David Lynch;David Fagan;Stepan Kucera;Holger Claussen;Michael O’Neill
2020 IEEE Congress on Evolutionary Computation (CEC)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
Grammar-Guided Genetic Programming is already outperforming humans at creating efficient transmission schedulers for large heterogeneous communications networks. We have previously proposed a multi-level grammar approach which achieved significantly better results than the canonical Grammar-Guided Genetic Programming approach. Initially, a restricted `small' grammar is utilised in order to discove...Show More
Optimising Team Sport Training Plans With Grammatical Evolution
2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
We present a novel approach to generating seasonal training plans for elite athletes using the grammatical evolution approach to genetic programming. A grammatical encoding of a team sport training plan dictates the plan structure. The quality of the training plan is calculated using the widely adopted fitness-fatigue model, which in this study incorporates four performance metrics, namely distanc...Show More
Mutational Robustness and Structural Complexity in Grammatical Evolution
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
How Learning Strategies Can Promote an Evolving Population in Dynamic Environments
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
A Survey of Statistical Machine Learning Elements in Genetic Programming
Alexandros Agapitos;Roisin Loughran;Miguel Nicolau;Simon Lucas;Michael O’Neill;Anthony Brabazon
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
Automated Self-Optimization in Heterogeneous Wireless Communications Networks
David Lynch;Michael Fenton;David Fagan;Stepan Kucera;Holger Claussen;Michael O’Neill
Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heurist...Show More
The Baldwin Effect Reconsidered Through the Prism of Social Learning
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
Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming
Stefan Forstenlechner;David Fagan;Miguel Nicolau;Michael O'Neill
2018 IEEE Congress on Evolutionary Computation (CEC)
Year: 2018 | Conference Paper |
Cited by: Papers (10)
Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general pro...Show More
Gems: A novel method to accelerate Evolutionary Algorithms
2017 IEEE Congress on Evolutionary Computation (CEC)
Year: 2017 | Conference Paper |
This paper presents gems, a novel method to accelerate fitness improvement in Evolutionary Algorithms (EAs). The paper develops the models, describes an experimental implementation, comments on characteristics of problem-domains that indicate where gems may be used, and suggests an explanation of the observed behavior. Experimental results show that gems accelerate the rate of fitness increase, an...Show More
Social Grammatical Evolution with imitation learning for real-valued function estimation
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
Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment
David Fagan;Michael Fenton;David Lynch;Stepan Kucera;Holger Claussen;Michael O'Neill
2017 International Joint Conference on Neural Networks (IJCNN)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) o...Show More
Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming
Michael Fenton;David Lynch;Stepan Kucera;Holger Claussen;Michael O’Neill
IEEE Transactions on Cybernetics
Cited by: Papers (11)
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 e...Show More
Evolutionary Behavior Tree Approaches for Navigating Platform Games
Miguel Nicolau;Diego Perez-Liebana;Michael O’Neill;Anthony Brabazon
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
An exploration of grammatical encodings to model six nations rugby match outcomes
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
Exploring position independent initialisation in grammatical evolution
2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (18)
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 initialisatio...Show More
Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution
Michael Fenton;Ciaran McNally;Jonathan Byrne;Erik Hemberg;James McDermott;Michael O’Neill
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 ca...Show More
Load balancing in heterogeneous networks using an evolutionary algorithm
Michael Fenton;David Lynch;Stepan Kucera;Holger Claussen;Michael O'Neill
2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Cited by: Papers (11)
Grammatical Evolution (GE) is applied to the problem of load balancing in heterogeneous cellular network deployments (HetNets). HetNets are multi-tiered cellular networks for which load balancing is a scalable means to maximise network capacity, assuming similar traffic from all users. This paper describes a proof of concept study in which GE is used in a genetic algorithm-like way to evolve const...Show More
Tonality driven piano compositions with grammatical evolution
2015 IEEE Congress on Evolutionary Computation (CEC)
Year: 2015 | Conference Paper |
Cited by: Papers (15)
We present a novel method of creating piano melodies with Grammatical Evolution (GE). The system employs a context free grammar in combination with a tonality-driven fitness function to create a population of piano melodies. The grammar is designed to create a variety of styles of musical events within each melody such as runs, arpeggios, turns and chords without any a priori musical information i...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
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
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
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
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
Wave height quantification using land based seismic data with grammatical evolution
2014 IEEE Congress on Evolutionary Computation (CEC)
Year: 2014 | Conference Paper |
Cited by: Papers (6)
Accurate, real time, continuous ocean wave height measurements are required for the initialisation of ocean wave forecast models, model hindcasting, and climate studies. These measurements are usually obtained using in situ ocean buoys or by satellite altimetry, but are sometimes incomplete due to instrument failure or routine network upgrades. In such situations, a reliable gap filling technique ...Show More
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