Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of size and shape
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming: A Review of Some Concerns
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Code growth in genetic programming
Code growth in genetic programming
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Finding state-of-the-art non-cryptographic hashes with genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
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In Genetic Programming (GP), One-Point Crossover is an alternative to the destructive properties and poor performance of Standard Crossover. One-Point Crossover acts in two phases, first making the population converge to a common tree shape, then looking for the best individual within that shape. So, we understand that One-Point Crossover is making an implicit evolution of tree shapes. We want to know if making this evolution explicit could lead to any improvement in the search power of GP. But we first need to define how this evolution could be performed. In this work we made an exhaustive study of fitness distributions of tree shapes for 6 different GP problems. We were able to identify common properties on distributions, and we propose a method to explicitly evaluate tree shapes. Based on this method, in the future, we want to implement a new genetic operator and a novel representation system for GP.