Research explorer tool

11 results

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...

On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains

Conference Proceeding
Stone, C., Hart, E., & Paechter, B. (2018)
On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains. In Parallel Problem Solving from Nature – PPSN XV 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part Ihttps://doi.org/10.1007/978-3-319-99253-2_14
Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, rely on a set of domain-specific low-level heuristics at lower levels. For some domains,...

Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs

Conference Proceeding
Stone, C., Hart, E., & Paechter, B. (2017)
Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs. In Applications of Evolutionary Computation, 578-593
In many industrial problem domains, when faced with a combinatorial optimisation problem, a “good enough, quick enough” solution to a problem is often required. Simple heurist...

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...

A research agenda for metaheuristic standardization.

Presentation / Conference
Hart, E., & Sim, K. (2015, June)
A research agenda for metaheuristic standardization. Paper presented at 11th Metaheuristics International Conference, Agadir, Morocco
We propose that the development of standardized, explicit, machine-readable descriptions of metaheuris- tics will greatly advance scientific progress in the field. In particul...

Biologcal Networks.

Book Chapter
Hart, E. (2011)
Biologcal Networks. In M. Gargaud, R. Amils, J. Quintanilla, & H. Irvine (Eds.), Encyclopedia of Astrobiology, 179-182. Springer-Verlag. https://doi.org/10.1007/978-3-642-11274-4_178

How affinity influences tolerance in an idiotypic network.

Journal Article
Hart, E., Bersini, H. & Santos, F. (2007)
How affinity influences tolerance in an idiotypic network. Journal of Theoretical Biology. 249, 422-436. doi:10.1016/j.jtbi.2007.07.019. ISSN 0022-5193
The mutability of bacteriophages offers a particular advantage in the treatment of bacterial infections not afforded by other antimicrobial therapies. When phage-resistant bac...

Evolutionary scheduling: a review.

Journal Article
Hart, E., Ross, P., & Corne, D. (2005)
Evolutionary scheduling: a review. Genetic Programming and Evolvable Machines, 6, 191-220. https://doi.org/10.1007/s10710-005-7580-7
Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been ...

Exploiting the analogy between the immune system and sparse distributed memory.

Journal Article
Hart, E., & Ross, P. (2002)
Exploiting the analogy between the immune system and sparse distributed memory. Genetic Programming and Evolvable Machines, 4(4), 333-358. doi:10.1023/a:1026191011609
The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for...

Hyper-heuristics: learning to combine simple heuristics in bin-packing problems.

Conference Proceeding
Ross, P., Schulenburg, S., Marin-Blazquez, J. G. & Hart, E. (2002)
Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. ISBN 1558608788
Evolutionary algorithms (EAs) often appear to be a ‘black box’, neither offering worst-case bounds nor any guarantee of optimality when used to solve individual problems. They...