Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Exploiting disruption aversion to control code bloat
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Diversity in genetic programming: an analysis of measures and correlation with fitness
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
Drawing boundaries: using individual evolved class boundaries for binary classification problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.