Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
On evolutionary exploration and exploitation
Fundamenta Informaticae
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A less destructive, context-aware crossover operator for GP
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Balancing Parent and Offspring Selection in Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Genetic programming with a norm-referenced fitness function
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. This paper reports on experiments demonstrating that premature convergence may happen more often when using these techniques in combination with standard parent selection. It shows that modifying the selection pressure in the parent selection process is necessary to obtain a significant performance improvement.