L
Leonardo Vanneschi
Researcher at Universidade Nova de Lisboa
Publications - 273
Citations - 4963
Leonardo Vanneschi is an academic researcher from Universidade Nova de Lisboa. The author has contributed to research in topics: Genetic programming & Genetic algorithm. The author has an hindex of 33, co-authored 243 publications receiving 4266 citations. Previous affiliations of Leonardo Vanneschi include University of Milan & Technical University of Lisbon.
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Proceedings ArticleDOI
Genetic programming needs better benchmarks
James McDermott,David White,Sean Luke,Luca Manzoni,Mauro Castelli,Leonardo Vanneschi,Wojciech Jaskowski,Krzysztof Krawiec,Robin Harper,Kenneth de Jong,Una-May O'Reilly +10 more
TL;DR: This paper argues that the definition of standard benchmarks is an essential step in the maturation of the field and motivates the development of a benchmark suite and defines its goals.
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Open issues in genetic programming
TL;DR: Some of the challenges and open issues that face researchers and practitioners of GP are outlined and it is hoped this overview will stimulate debate, focus the direction of future research to deepen the understanding of GP, and further the development of more powerful problem solving algorithms.
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A survey of semantic methods in genetic programming
TL;DR: This survey analyzes and discusses the state of the art in the field, organizing the existing methods into different categories, and restricts itself to studies where semantics is intended as the set of output values of a program on the training data.
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An Empirical Study of Multipopulation Genetic Programming
TL;DR: It is found that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations.
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Measuring bloat, overfitting and functional complexity in genetic programming
TL;DR: Three measures to respectively quantify bloat, overfitting and functional complexity of solutions and show their suitability on a set of test problems including a simple bidimensional symbolic regression test function and two real-life multidimensional regression problems are defined.