selected publications
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academic article
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academic article in scopus
- A feature-independent hyper-heuristic approach for solving the knapsack problem. Applied Sciences (Switzerland). 11. 2021
- Hyper-Heuristics to customise metaheuristics for continuous optimisation. Swarm and Evolutionary Computation. 66. 2021
- A general framework based on machine learning for algorithm selection in constraint satisfaction problems. Applied Sciences (Switzerland). 11. 2021
- Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic. Computational Intelligence and Neuroscience. 2021. 2021
- A Fuzzy Hyper-Heuristic Approach for the 0-1 Knapsack Problem 2020
- A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem 2020
- A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous optimisation 2020
- Evolutionary-based tailoring of synthetic instances for the Knapsack problem. Soft Computing. 23:12711-12728. 2019
- Influence of Instance Size on Selection Hyper-Heuristics for Job Shop Scheduling Problems 2019
- A Simulated Annealing Hyper-heuristic for Job Shop Scheduling Problems 2019
- Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances 2019
- Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning. Expert Systems with Applications. 118:470-481. 2019
- Enhancing Selection Hyper-Heuristics via Feature Transformations. IEEE Computational Intelligence Magazine. 13:30-41. 2018
- Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems. Computational Intelligence and Neuroscience. 2018. 2018
- Applying automatic heuristic-filtering to improve hyper-heuristic performance 2017
- Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems. Artificial Intelligence Review. 46:327-349. 2016
- Grammar-based selection hyper-heuristics for solving irregular bin packing problems 2016
- A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems. Cognitive Computation. 8:429-441. 2016
- Grammar-based generation of variable-selection heuristics for constraint satisfaction problems. Genetic Programming and Evolvable Machines. 17:119-144. 2016
- A recursive split, solve, and join strategy for solving constraint satisfaction problems 2016
- Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems. Computational Intelligence and Neuroscience. 2016. 2016
- Decomposition and recomposition strategies to solve timetabling problems 2014
- Understanding the structure of bin packing problems through principal component analysis. International Journal of Production Economics. 145:488-499. 2013
- Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space 2013
- Using learning classifier systems to design selective hyper-heuristics for constraint satisfaction problems 2013
- Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recognition Letters. 34:423-432. 2013
- The impact of the bin packing problem structure in hyper-heuristic performance 2012
- Improving the performance of vector hyper-heuristics through local search 2012
- Challenging heuristics: Evolving binary constraint satisfaction problems 2012
- Evolution of neural networks topologies and learning parameters to produce hyper-heuristics for constraint satisfaction problems 2011
- Evolving solutions of mixed-model assembly line balancing problems by chaining heuristic optimization methods: Track: Evolutionary combinatorial optimization and meta-heuristics 2011
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book in scopus
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chapter in scopus
- Tailoring instances of the 1d bin packing problem for assessing strengths and weaknesses of its solvers. Lecture Notes in Computer Science. 373-384. 2018
- Lifelong learning selection hyper-heuristics for constraint satisfaction problems. Lecture Notes in Computer Science. 190-201. 2015
- Assembling similar tracking approaches in order to strengthen performance. Lecture Notes in Computer Science. 201-210. 2014
- Branching schemes and variable ordering heuristics for constraint satisfaction problems: Is there something to learn?. Studies in Computational Intelligence. 329-342. 2014
- A supervised learning approach to construct hyper-heuristics for constraint satisfaction. Lecture Notes in Computer Science. 284-293. 2013
- Exploring the solution of course timetabling problems through heuristic segmentation. Lecture Notes in Computer Science. 347-358. 2013
- Local features classification for adaptive tracking. Lecture Notes in Computer Science. 146-157. 2013
- Variable and value ordering decision matrix hyper-heuristics: A local improvement approach. Lecture Notes in Computer Science. 125-136. 2011
- Neural networks to guide the selection of heuristics within constraint satisfaction problems. Lecture Notes in Computer Science. 250-259. 2011