Outputs (89)

An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics (2017)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2017). An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference (155-162).

A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. However, the role played by the environment in influencing the effectiveness o... Read More about An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics.

Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation (2016)
Presentation / Conference Contribution
Segredo, E., Lalla-Ruiz, E., Hart, E., Paechter, B., & Voß, S. (2016, May). Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation. Presented at Learning and Intelligent OptimizatioN Conference LION 10, Ischi

Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search.... Read More about Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation.

Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems (2016)
Presentation / Conference Contribution
Segredo, E., Paechter, B., Hart, E., & Gonz´alez-Vila, C. I. (2016). Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems. In 2016 IEEE Congress on Evolutionary Computation (C

In order to address the difficult issue of parameter setting within a diversity-based Multi-objective Evolutionary Algorithm (MOEA), we recently proposed a hybrid control scheme based on both Fuzzy Logic Controllers (FLCs) and Hyper-heuristics (HHs).... Read More about Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems.

Trickle-Plus: Elastic Trickle algorithm for Low-power networks and Internet of Things (2016)
Presentation / Conference Contribution
Ghaleb, B., Al-Dubai, A., Ekonomou, E., Paechter, B., & Qasem, M. (2016, April). Trickle-Plus: Elastic Trickle algorithm for Low-power networks and Internet of Things. Presented at 2016 IEEE Wireless Communications and Networking Conference

Constrained Low-power and Lossy networks (LLNs) represent the building block for the ever-growing Internet of Things (IoT) that deploy the Routing Protocol for Low Power and Lossy networks (RPL) as a key routing standard. RPL, along with other routin... Read More about Trickle-Plus: Elastic Trickle algorithm for Low-power networks and Internet of Things.

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm (2016)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2016, October). Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. Presented at PPSN 2016 14th International Conference on Parallel Problem Solving from Nature

It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear exactly how parameterisation of a given environment might influence the emer... Read More about Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm.

Analysing the performance of migrating birds optimisation approaches for large scale continuous problems (2016)
Presentation / Conference Contribution
Lalla-Ruiz, E., Segredo, E., Voss, S., Hart, E., & Paechter, B. (2016, September). Analysing the performance of migrating birds optimisation approaches for large scale continuous problems. Presented at 14th International Conference on Parallel Problem Sol

We present novel algorithmic schemes for dealing with large scale continuous problems. They are based on the recently proposed population-based meta-heuristics Migrating Birds Optimisation (mbo) and Multi-leader Migrating Birds Optimisation (mmbo), t... Read More about Analysing the performance of migrating birds optimisation approaches for large scale continuous problems.

Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. (2015)
Presentation / Conference Contribution
Hart, E., Steyven, A., & Paechter, B. (2015). Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. In Proceedings of the 2015 on Genetic and Evolution

Ensuring the integrity of a robot swarm in terms of maintaining a stable population of functioning robots over long periods of time is a mandatory prerequisite for building more complex systems that achieve user-defined tasks. mEDEA is an environ... Read More about Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication..

The Cost of Communication: Environmental Pressure and Survivability in mEDEA (2015)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2015, July). The Cost of Communication: Environmental Pressure and Survivability in mEDEA. Presented at GECCO ’15

We augment the mEDEA algorithm to explicitly account for the costs of communication between robots. Experimental results show that adding a costs for communication exerts environmental pressure to implicitly select for genomes that maintain high... Read More about The Cost of Communication: Environmental Pressure and Survivability in mEDEA.

A Lifelong Learning Hyper-heuristic Method for Bin Packing (2015)
Journal Article
Hart, E., Sim, K., & Paechter, B. (2015). A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evolutionary Computation, 23(1), 37-67. https://doi.org/10.1162/EVCO_a_00121

We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; representative problems and heur... Read More about A Lifelong Learning Hyper-heuristic Method for Bin Packing.

Learning to solve bin packing problems with an immune inspired hyper-heuristic. (2013)
Presentation / Conference Contribution
Sim, K., Hart, E., & Paechter, B. (2013). Learning to solve bin packing problems with an immune inspired hyper-heuristic. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artificial Life, ECAL 2013 (856-863). https://doi.org/1

Motivated by the natural immune system's ability to defend the body by generating and maintaining a repertoire of antibodies that collectively cover the potential pathogen space, we describe an artificial system that discovers and maintains a reperto... Read More about Learning to solve bin packing problems with an immune inspired hyper-heuristic..