Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Effects of locality in individual and population evolution
Advances in genetic programming
Advances in genetic programming
The evolution of size and shape
Advances in genetic programming
Advanced Population Diversity Measures in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Code growth in genetic programming
Code growth in genetic programming
Dormant program nodes and the efficiency of genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the behavioral diversity of random programs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
Semantically driven mutation in genetic programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Characterizing diversity in genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
Mutation as a diversity enhancing mechanism in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Maintaining population diversity in evolutionary art
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
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Population diversity is generally seen as playing a crucial role in the ability of evolutionary computation techniques to discover solutions. In genetic programming, diversity metrics are usually based on structural properties of individual program trees, but are also sometimes based on the spread of fitness values in the population. We explore the use of a further interpretation of diversity, in which differences are measured in terms of the behaviour of programs when executed. Although earlier work has shown that improving behavioural diversity in initial GP populations can have a marked beneficial effect on performance, further analysis reveals that lack of behavioural diversity is a problem throughout whole runs, even when other diversity levels are high. To address this, we enhance phenotypic diversity via modifications to the crossover operator, and show that this can lead to additional performance improvements.