C
Christian Blum
Researcher at Spanish National Research Council
Publications - 253
Citations - 13596
Christian Blum is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Metaheuristic & Ant colony optimization algorithms. The author has an hindex of 37, co-authored 227 publications receiving 12281 citations. Previous affiliations of Christian Blum include Ikerbasque & Polytechnic University of Catalonia.
Papers
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Book ChapterDOI
Hybridization Based on Large Neighborhood Search
Christian Blum,Günther R. Raidl +1 more
TL;DR: The type of algorithm addressed in this chapter is based on the following general idea: Given a valid solution to the tackled problem instance, first, destroy selected parts of it, resulting in a partial solution, which is applied to a MIP model for the original problem in which the variables corresponding to the given partial solution get respective fixed values preassigned.
Journal ArticleDOI
Computational Performance Evaluation of Two Integer Linear Programming Models for the Minimum Common String Partition Problem
TL;DR: In this paper, a new, alternative integer linear programming (ILP) model was proposed to solve the minimum common string partition (MCSP) problem, where two related input strings are given.
Hybrid metaheuristics: 10th International Workshop, HM 2016, Plymouth, UK, June 8-10, 2016. Proceedings
Maria J. Blesa,Christian Blum,Angelo Cangelosi,Vincenzo Cutello,Alessandro Di Nuovo,Mario Pavone,El-Ghazali Talbi +6 more
A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem
TL;DR: A novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics is presented, which is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP.
Proceedings ArticleDOI
Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks
TL;DR: The obtained results show that the hybrid algorithm is able to outperform both the biased random key genetic algorithm and the graph neural network when used as standalone techniques, showing that an integration of both techniques leads to a better algorithm.