Genetic Programming and Evolvable Machines
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Seeding Genetic Programming Populations
Proceedings of the European Conference on Genetic Programming
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
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
GECCO '05 Proceedings of the 7th annual conference on Genetic and 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
Evolving encapsulated programs as shared grammars
Genetic Programming and Evolvable Machines
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
The influence of mutation on population dynamics in multiobjective genetic programming
Genetic Programming and Evolvable Machines
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
Robotic path planning using hybrid genetic algorithm particle swarm optimisation
International Journal of Information and Communication Technology
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It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine learning problems. We conclude that mutation is an important -- and we believe a hitherto unrecognized -- factor in preventing population collapse in multiobjective genetic programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse.