The evolution of evolvability in genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
A new approach to evaluate GP schema in context
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A less destructive, context-aware crossover operator for GP
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Context-aware mutation: a modular, context aware mutation operator for genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the constructiveness of context-aware crossover
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A swarm-based crossover operator for genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GP age-layer and crossover effects in bid-offer spread prediction
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Testing the CAX on a Real-World Problem and Other Benchmarks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Program optimization by random tree sampling
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
Positional effect of crossover and mutation in grammatical evolution
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Prioritized grammar enumeration: symbolic regression by dynamic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper describes the use of a recently introduced crossover operator for GP, context-aware crossover. Given a randomly selected subtree from one parent, context-aware crossover will always find the best location to place the subtree in the other parent.We examine the performance of GP when context-aware crossover is used as an extra crossover operator, and show that standard crossover is far more destructive, and that performance is better when only context-aware crossover is used.There is still a place for standard crossover, however, and results suggest that using standard crossover in the initial part of the run and then switching to context-aware crossover yields the best performance.We show that, across a range of standard GP benchmark problems, context-aware crossover produces a higher best fitness as well as a higher mean fitness, and even manages to solve the 11-bit multiplexer problem without ADFs. Furthermore, the individuals produced this way are much smaller than standard GP, and far fewer individual evaluations are required, so GP achieves a higher fitness by evaluating fewer and smaller individuals.