Trickle-Plus: Elastic Trickle algorithm for Low-power networks and Internet of Things
Conference Proceeding
Ghaleb, B., Al-Dubai, A., Ekonomou, E., Paechter, B., & Qasem, M. (2016)
Trickle-Plus: Elastic Trickle algorithm for Low-power networks and Internet of Things. In Wireless Communications and Networking Conference (WCNC), 2016 IEEE (1-6). https://doi.org/10.1109/WCNC.2016.7564654
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 L...
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2016)
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science. , (921-931). https://doi.org/10.1007/978-3-319-45823-6_86
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 e...
Analysing the performance of migrating birds optimisation approaches for large scale continuous problems
Conference Proceeding
Lalla-Ruiz, E., Segredo, E., Voss, S., Hart, E., & Paechter, B. (2016)
Analysing the performance of migrating birds optimisation approaches for large scale continuous problems. In Parallel Problem Solving from Nature – PPSN XIV. , (134-144). https://doi.org/10.1007/978-3-319-45823-6_13
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...
Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication.
Conference Proceeding
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 Evolutionary Computation Conference - GECCO '15, (169-176). https://doi.org/10.1145/2739480.2754688
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 mo...
The Cost of Communication: Environmental Pressure and Survivability in mEDEA
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2015)
The Cost of Communication: Environmental Pressure and Survivability in mEDEA. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 1239-1240. doi:10.1145/2739482.2768489
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
envi...
Learning to solve bin packing problems with an immune inspired hyper-heuristic.
Conference Proceeding
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/10.7551/978-0-262-31709-2-ch126
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 spa...
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution.
Conference Proceeding
Sim, K., Hart, E., & Paechter, B. (2012)
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In Parallel Problem Solving from Nature: PPSN XII, (348-357). https://doi.org/10.1007/978-3-642-32964-7_35
A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem i...
Towards self-aware PerAda systems.
Conference Proceeding
Hart, E., & Paechter, B. (2010)
Towards self-aware PerAda systems. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), Artificial Immune Systems: 9th International Conference, ICARIS 2010 Proceedings, 314-216. https://doi.org/10.1007/978-3-642-14547-6_28
Pervasive Adaptation (PerAda) refers to massive-scale pervasive information and communication systems which are capable of autonomously adapting to highly dynamic and open tec...
Strengthening the Forward Variable Selection Stopping Criterion
Conference Proceeding
Herrera, L. J., Rubio, G., Pomares, H., Paechter, B., Guillén, A., & Rojas, I. (2009)
Strengthening the Forward Variable Selection Stopping Criterion. In Artificial Neural Networks – ICANN 2009. , (215-224). https://doi.org/10.1007/978-3-642-04277-5_22
Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most...
Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab
Conference Proceeding
Rubio, G., Guillen, A., Pomares, H., Rojas, I., Paechter, B., Glosekotter, P., & Torres-Ceballos, C. I. (2009)
Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab. In HPCS '09. International Conference on High Performance Computing & Simulation, 2009https://doi.org/10.1109/hpcsim.2009.5192804
The kernel weighted k-nearest neighbours (KWKNN) algorithm is an efficient kernel regression method that achieves competitive results with lower computational complexity than ...