Research explorer tool

12 results

On artificial immune systems and swarm intelligence

Journal Article
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...

Representation in the (Artificial) Immune System

Journal Article
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

Journal Article
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...

Structure versus function: a topological perspective on immune networks

Journal Article
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...

A Lifelong Learning Hyper-heuristic Method for Bin Packing.

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 ...

A hyper-heuristic ensemble method for static job-shop scheduling.

Journal Article
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...

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

Journal Article
Segredo, E., Seguro, C., Coromoto, L., & Hart, E. (2014)
A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation. Soft Computing, 19(10), 2927-2945. https://doi.org/10.1007/s00500-014-1454-y
In recent years, Multi-Objective Evolutionary Algorithms (MOEAS) that consider diversity as an objective have been used to tackle single-objective optimisation prob- lems. The...

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Journal Article
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 ...

Selection methods and diversity preservation in many-objective evolutionary algorithms

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...

Impact of selection methods on the diversity of many-objective Pareto set approximations

Journal Article
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (2017)
Impact of selection methods on the diversity of many-objective Pareto set approximations. Procedia Computer Science, 112, (844-853). ISSN 1877-0509
Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultane...