All Outputs (147)

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

On Pros and Cons of Evolving Topologies with Novelty Search (2020)
Presentation / Conference Contribution
Le Goff, L. K., Hart, E., Coninx, A., & Doncieux, S. (2020). On Pros and Cons of Evolving Topologies with Novelty Search. In ALIFE 2020: The 2020 Conference on Artificial Life (423-431). https://doi.org/10.1162/isal_a_00291

Novelty search was proposed as a means of circumventing deception and providing selective pressure towards novel behaviours to provide a path towards open-ended evolution. Initial implementations relied on neuro-evolution approaches which increased n... Read More about On Pros and Cons of Evolving Topologies with Novelty Search.

Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation (2020)
Presentation / Conference Contribution
Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., Hale, M. F., Angus, M., Woolley, R., Timmis, J., Winfield, A., & Tyrrell, A. M. (2020, July). Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation. Presented at ALife 2020, Online

In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a 'newborn' robot in order to better align it to its... Read More about Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation.

A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains (2020)
Presentation / Conference Contribution
Alissa, M., Sim, K., & Hart, E. (2020, July). A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains. Presented at GECCO ’20, Cancún, Mexico

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict th... Read More about A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains.

From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies (2020)
Journal Article
Perret, C., Hart, E., & Powers, S. T. (2020). From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies. Proceedings of the Royal Society B: Biological Sciences, 287(1928), Article 20200693. https://doi.org/10.1098/rspb.2020.0693

A manifest trend is that larger and more productive human groups shift from distributed to centralized decision-making. Voluntary theories propose that human groups shift to hierarchy to limit scalar stress, i.e. the increase in cost of organization... Read More about From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies.

Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites (2020)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2020, April). Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. Presented at EvoStar 2020, Seville, Spain

In the field of metamorphic malware detection, training a detection model with malware samples that reflect potential mutants of the malware is crucial in developing a model resistant to future attacks. In this paper, we use a Multi-dimensional Archi... Read More about Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites.

Using MAP-Elites to support policy making around Workforce Scheduling and Routing (2020)
Journal Article
Urquhart, N., Hart, E., & Hutcheson, W. (2020). Using MAP-Elites to support policy making around Workforce Scheduling and Routing. Automatisierungstechnik, 68(2), https://doi.org/10.1515/auto-2019-0107

English abstract: Algorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an archive, can allow the user an overview of the solution space which may be use... Read More about Using MAP-Elites to support policy making around Workforce Scheduling and Routing.

A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution (2019)
Journal Article
Segredo, E., Lalla-Ruiz, E., Hart, E., & Voß, S. (2020). A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution. Computers and Operations Research, 117, Article 104871. https://doi.org/10.1016/j.cor.2019.104871

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to a... Read More about A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution.

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2019, November). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. Presented at The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019), Guangzhou, China

The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection syste... Read More about Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World (2019)
Presentation / Conference Contribution
Hale, M. F., Buchanan, E., Winfield, A. F., Timmis, J., Hart, E., Eiben, A. E., Angus, M., Veenstra, F., Li, W., Woolley, R., De Carlo, M., & Tyrrell, A. M. (2019, July). The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World. Presented at Artificial Life, Newcastle, UK

The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of both virtual and physical robots with evolving brains and bodies. One of the major challenges for such a vision is the need to construct many unique ind... Read More about The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World.

Being a leader or being the leader: The evolution of institutionalised hierarchy (2019)
Presentation / Conference Contribution
Perret, C., Hart, E., & Powers, S. T. (2019, July). Being a leader or being the leader: The evolution of institutionalised hierarchy. Presented at ALIFE 2019, Newcastle upon Tyne

Human social hierarchy has the unique characteristic of existing in two forms. Firstly, as an informal hierarchy where leaders and followers are implicitly defined by their personal characteristics, and secondly, as an institutional hierarchy where l... Read More about Being a leader or being the leader: The evolution of institutionalised hierarchy.

Evolving robust policies for community energy system management (2019)
Presentation / Conference Contribution
Cardoso, R., Hart, E., & Pitt, J. (2019, July). Evolving robust policies for community energy system management. Presented at GECCO '19, Prague, Czech Republic

Community energy systems (CESs) are shared energy systems in which multiple communities generate and consume energy from renewable resources. At regular time intervals, each participating community decides whether to self-supply, store, trade, or sel... Read More about Evolving robust policies for community energy system management.

An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem (2019)
Presentation / Conference Contribution
Urquhart, N., Hoehl, S., & Hart, E. (2019, July). An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem. Presented at Genetic and Evolutionary Computation Conference (GECCO '19), Prague, Czech Republic

An increasing emphasis on reducing pollution and congestion in city centres combined with an increase in online shopping is changing the ways in which logistics companies address vehicle routing problems (VRP). We introduce the {\em micro-depot}-VRP,... Read More about An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem.

Comparing encodings for performance and phenotypic exploration in evolving modular robots (2019)
Presentation / Conference Contribution
Veenstra, F., Hart, E., Buchanan, E., Li, W., De Carlo, M., & Eiben, A. E. (2019, July). Comparing encodings for performance and phenotypic exploration in evolving modular robots. Presented at GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic

To investigate how encodings influence evolving the morphology and control of modular robots, we compared three encodings: a direct encoding and two generative encodings---a compositional pattern producing network (CPPN) and a Lindenmayer System (L-S... Read More about Comparing encodings for performance and phenotypic exploration in evolving modular robots.

Algorithm selection using deep learning without feature extraction (2019)
Presentation / Conference Contribution
Alissa, M., Sim, K., & Hart, E. (2019). Algorithm selection using deep learning without feature extraction. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (198-206). https://doi.org/10.1145/3321707.3321845

We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does... Read More about Algorithm selection using deep learning without feature extraction.

Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. (2019)
Presentation / Conference Contribution
Urquhart, N., Hart, E., & Hutcheson, W. (2019, April). Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. Presented at EvoStar2019: International Conference on the Applications of Evolutionary Computation, Leipzig

Quality-diversity algorithms such as MAP-Elites provide a means of supporting the users when finding and choosing solutions to a problem by returning a set of solutions which are diverse according to set of user-defined features. The number of soluti... Read More about Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem..

Use of machine learning techniques to model wind damage to forests (2018)
Journal Article
Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. (2019). Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265, 16-29. https://doi.org/10.1016/j.agrformet.2018.10.022

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed i... Read More about Use of machine learning techniques to model wind damage to forests.

Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments (2018)
Presentation / Conference Contribution
Cardoso, R. P., Rossetti, R. J. F., Hart, E., Kurka, D. B., & Pitt, J. (2018, November). Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments. Presented at 8th International Symposium, ISoLA 2018, Limassol, Cyprus

Electronic institutions are socially-inspired multi-agent systems, typically operating under a set of policies, which are required to determine system operation and to deal with violations and other non-compliant behaviour. They are often faced with... Read More about Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments.

Selection methods and diversity preservation in many-objective evolutionary algorithms (2018)
Journal Article
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (2018). Selection methods and diversity preservation in many-objective evolutionary algorithms. Data Technologies and Applications, https://doi.org/10.1108/dta-01-2018-0009

Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecti... Read More about Selection methods and diversity preservation in many-objective evolutionary algorithms.

Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites (2018)
Presentation / Conference Contribution
Urquhart, N., & Hart, E. (2018, September). Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites. Presented at Parallel Problem Solving from Nature (PPSN) 2018, Coimbra, Portugal

Workforce Scheduling and Routing Problems (WSRP) are very common in many practical domains, and usually have a number of objectives. Illumination algorithms such as Map-Elites (ME) have recently gained traction in application to design problems, in p... Read More about Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites.