Parallel genetic algorithms for a hypercube
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
An Empirical Study of Multipopulation Genetic Programming
Genetic Programming and Evolvable Machines
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
On the behavioral diversity of random programs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
Representation and structural difficulty in genetic programming
IEEE Transactions on Evolutionary Computation
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Population diversity has long been seen as a crucial factor for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. This paper experimentally investigates the diversity property of a recently proposed crossover, Semantic Similarity based Crossover (SSC). The results show that while SSC helps to improve locality, it leads to the loss of diversity of the population. This could be the reason that sometimes SSC fails in achieving superior performance when compared to standard subtree crossover. Consequently, we introduce an approach to maintain the population diversity by combining SSC with a multi-population approach. The experimental results show that this combination maintains better population diversity, leading to further improvement in GP performance. Further SSC parameters tuning to promote diversity gains even better results.