Ivo Gonçalves
Carlos M. Fonseca
ABSTRACT
This paper shows how arbitrarily close alignments in the error space can be achieved by Genetic Programming. The consequences for the generalization ability of the resulting individuals are explored.
- I. Gonçalves, S. Silva, and C. M. Fonseca. On the generalization ability of geometric semantic genetic programming. In Genetic Programming, pages 41--52. Springer, 2015.Google Scholar
- I. Gonçalves, S. Silva, and C. M. Fonseca. Semantic learning machine: A feedforward neural network construction algorithm inspired by geometric semantic genetic programming. In Progress in Artificial Intelligence, volume 9273 of Lecture Notes in Computer Science, pages 280--285. Springer International Publishing, 2015.Google Scholar
- A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. In Parallel Problem Solving from Nature-PPSN XII, pages 21--31. Springer, 2012. Google ScholarDigital Library
- S. Ruberto, L. Vanneschi, M. Castelli, and S. Silva. Esagp--a semantic gp framework based on alignment in the error space. In Genetic Programming, pages 150--161. Springer, 2014. Google ScholarDigital Library
Index Terms
- Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach
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