Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Discovery of subroutines in genetic programming
Advances in genetic programming
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Foundations of genetic programming
Foundations of genetic programming
Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Genetic programming for cross-task knowledge sharing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Schema theory for genetic programming with one-point crossover and point mutation
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
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
Semantically driven mutation in genetic programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Journal of Computer and System Sciences
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Semantic similarity based crossover in GP: the case for real-valued function regression
EA'09 Proceedings of the 9th international conference on Artificial evolution
Abstract convex evolutionary search
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Geometric nelder-mead algorithm on the space of genetic programs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Reassembling operator equalisation: a secret revealed
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Medial crossovers for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Locally geometric semantic crossover
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Approximating geometric crossover by semantic backpropagation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Guiding function set selection in genetic programming based on fitness landscape analysis
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This study presents an extensive account of Locally Geometric Semantic Crossover (LGX), a semantically-aware recombination operator for genetic programming (GP). LGX is designed to exploit the semantic properties of programs and subprograms, in particular the geometry of semantic space that results from distance-based fitness functions used predominantly in GP. When applied to a pair of parents, LGX picks in them at random a structurally common (homologous) locus, calculates the semantics of subprograms located at that locus, finds a procedure that is semantically medial with respect to these subprograms, and replaces them with that procedure. The library of procedures is prepared prior to the evolutionary run and indexed by a multidimensional structure (kd-tree) allowing for efficient search. The paper presents the rationale for LGX design and an extensive computational experiment concerning performance, computational cost, impact on program size, and capability of generalization. LGX is compared with six other operators, including conventional tree-swapping crossover, semantic-aware operators proposed in previous studies, and control methods designed to verify the importance of homology and geometry of the semantic space. The overall conclusion is that LGX, thanks to combination of the semantically medial operation with homology, improves the efficiency of evolutionary search, lowers the variance of performance, and tends to be more resistant to overfitting.