caa a22 4500 463206983 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20070301xx s 000 0 eng 10.1007/s10462-008-9093-2 doi (NATIONALLICENCE)springer-10.1007/s10462-008-9093-2 A Comparison of three evolutionary strategies for multiobjective genetic programming [Elektronische Daten] [Yang Zhang, Peter Rockett] We report what we believe to be the first comparative study of multi-objective genetic programming (GP) algorithms on benchmark symbolic regression and machine learning problems. We compare the Strength Pareto Evolutionary Algorithm (SPEA2), the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Pareto Converging Genetic Algorithm (PCGA) evolutionary paradigms. As well as comparing the quality of the final solutions, we also examine the speed of convergence of the three evolutionary algorithms. Based on our observations, the SPEA2-based algorithm appears to have problems controlling tree bloat—that is, the uncontrolled growth in the size of the chromosomal tree structures. The NSGA-II-based algorithm on the other hand seems to experience difficulties in locating low error solutions. Overall, the PCGA-based algorithm gives solutions with the lowest errors and the lowest mean complexity. Springer Science+Business Media B.V., 2008 Genetic programming nationallicence Multiobjective optimisation nationallicence Symbolic regression nationallicence Machine learning nationallicence Zhang Yang Vision and Information Engineering Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK aut Rockett Peter Vision and Information Engineering Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK aut Artificial Intelligence Review Springer Netherlands 27/2-3(2007-03-01), 149-163 0269-2821 27:2-3<149 2007 27 10462 https://doi.org/10.1007/s10462-008-9093-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-008-9093-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Yang Vision and Information Engineering Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK aut NATIONALLICENCE 700 1- Rockett Peter Vision and Information Engineering Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 27/2-3(2007-03-01), 149-163 0269-2821 27:2-3<149 2007 27 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer