Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
The nature of statistical learning theory
The nature of statistical learning theory
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Programming and Evolvable Machines
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
What makes a problem GP-hard? validating a hypothesis of structural causes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
Evolutionary multi objective optimization for rule mining: a review
Artificial Intelligence Review
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
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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.