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Andrea Mambrini
Andrea Mambrini
Exogene
Verified email at exogene.co.uk - Homepage
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Cited by
Cited by
Year
Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression
A Moraglio, A Mambrini
Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013
352013
Design and analysis of schemes for adapting migration intervals in parallel evolutionary algorithms
A Mambrini, D Sudholt
Evolutionary computation 23 (4), 559-582, 2015
252015
PaDe: A parallel algorithm based on the MOEA/D framework and the island model
A Mambrini, D Izzo
International Conference on Parallel Problem Solving from Nature, 711-720, 2014
242014
Design and analysis of adaptive migration intervals in parallel evolutionary algorithms
A Mambrini, D Sudholt
Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014
222014
Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions
A Moraglio, A Mambrini, L Manzoni
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII …, 2013
222013
Homogeneous and Heterogeneous Island Models for the Set Cover Problem
A Mambrini, D Sudholt, X Yao
Parallel Problem Solving from Nature-PPSN XII, 11-20, 2012
192012
On the analysis of simple genetic programming for evolving Boolean functions
A Mambrini, PS Oliveto
Genetic Programming: 19th European Conference, EuroGP 2016, Porto, Portugal …, 2016
152016
Theory-laden design of mutation-based geometric semantic genetic programming for learning classification trees
A Mambrini, L Manzoni, A Moraglio
2013 IEEE Congress on Evolutionary Computation, 416-423, 2013
82013
A comparison between geometric semantic GP and cartesian GP for Boolean functions learning
A Mambrini, L Manzoni
Proceedings of the Companion Publication of the 2014 Annual Conference on …, 2014
52014
PRINCIPIA: a decentralized peer-review ecosystem
A Mambrini, A Baronchelli, M Starnini, D Marinazzo, M De Domenico
arXiv preprint arXiv:2008.09011, 2020
12020
Theory grounded design of genetic programming and parallel evolutionary algorithms
A Mambrini
University of Birmingham, 2015
2015
A framework for measuring the generalization ability of Geometric Semantic Genetic Programming (GSGP) for Black-Box Boolean Functions Learning
A Mambrini, Y Yu, X Yao
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Articles 1–12