Selection methods and diversity preservation in many-objective evolutionary algorithms
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...
Use of machine learning techniques to model wind damage to forests
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 r...
Representation in the (Artificial) Immune System
McEwan, C., & Hart, E. (2009)
Representation in the (Artificial) Immune System. Journal of Mathematical Modelling and Algorithms, 8, 125-149. https://doi.org/10.1007/s10852-009-9104-6
Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausibl...
On Constructing Ensembles for Combinatorial Optimisation
Hart, E., & Sim, K. (2018)
On Constructing Ensembles for Combinatorial Optimisation. Evolutionary Computation, 26(1), 67-87. https://doi.org/10.1162/evco_a_00203
Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algori...
On artificial immune systems and swarm intelligence
Timmis, J., Andrews, P., & Hart, E. (2010)
On artificial immune systems and swarm intelligence. Swarm Intelligence, 4(4), 247-273. https://doi.org/10.1007/s11721-010-0045-5
This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are man...
On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems
Voß, S., Segredo, E., Lalla-Ruiz, E., Hart, E., & Voss, S. (2018)
On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems. Expert Systems with Applications, 102, 126-142. https://doi.org/10.1016/j.eswa.2018.02.024
Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More ...
Structure versus function: a topological perspective on immune networks
Hart, E., Bersini, H., & Santos, F. (2009)
Structure versus function: a topological perspective on immune networks. Natural Computing, https://doi.org/10.1007/s11047-009-9138-8
Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex...
Application areas of AIS: The past, the present and the future
Hart, E., & Timmis, J. (2008)
Application areas of AIS: The past, the present and the future. Applied Soft Computing, 8(1), 191-201. doi:10.1016/j.asoc.2006.12.004
After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the
paradigm has brought to t...
A Lifelong Learning Hyper-heuristic Method for Bin Packing.
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 ...
A hyper-heuristic ensemble method for static job-shop scheduling.
Hart, E., & Sim, K. (2016)
A hyper-heuristic ensemble method for static job-shop scheduling. Evolutionary Computation, 24(4), 609-635. https://doi.org/10.1162/EVCO_a_00183
We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conq...