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...
Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems
Conference Proceeding
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 (CEC)https://doi.org/10.1109/CEC.2016.7743969
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 sche...
A Cooperative Learning Approach for the Quadratic Knapsack Problem
Conference Proceeding
Lalla-Ruiz, E., Segredo, E., & Voß, S. (2018)
A Cooperative Learning Approach for the Quadratic Knapsack Problem. In Learning and Intelligent Optimization Conference (LION12). , (31-35). https://doi.org/10.1007/978-3-030-05348-2_3
The Quadratic Knapsack Problem (QKP) is a well-known optimization problem aimed to maximize a quadratic objective function subject to linear capacity constraints. It has sever...
Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation
Conference Proceeding
Segredo, E., Lalla-Ruiz, E., Hart, E., Paechter, B., & Voß, S. (2016)
Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016 (296-305). https://doi.org/10.1007/978-3-319-50349-3_25
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 whic...
On the comparison of initialisation strategies in differential evolution for large scale optimisation
Journal Article
Segredo, E., Paechter, B., Segura, C., & González-Vila, C. I. (2018)
On the comparison of initialisation strategies in differential evolution for large scale optimisation. Optimization Letters, 12(1), 221-234. https://doi.org/10.1007/s11590-017-1107-z
Differential Evolution (DE) has shown to be a promising global opimisation solver for continuous problems, even for those with a large dimensionality. Different previous works...
A novel similarity-based mutant vector generation strategy for differential evolution
Conference Proceeding
Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018)
A novel similarity-based mutant vector generation strategy for differential evolution. In H. Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference 2018https://doi.org/10.1145/3205455.3205628
The mutant vector generation strategy is an essential component of Differential Evolution (DE), introduced to promote diversity, resulting in exploration of novel areas of the...
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...
Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation
Journal Article
Segredo, E., Luque, G., Segura, C., & Alba, E. (2019)
Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation. IEEE Access, 7, 43915-43932. https://doi.org/10.1109/ACCESS.2019.2908562
Traffic congestion, and the consequent loss of time, money, quality of life, and higher pollution, is currently one of the most important problems in cities, and several approa...
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...
The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles
Conference Proceeding
Segura, C., Segredo, E., & Miranda, G. (2017)
The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles. In 2017 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2017.7969565
In recent years, several memetic algorithms with explicit mechanisms to delay convergence have shown great promise when solving 9x9 Sudoku puzzles. This paper analyzes and ext...