Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Advances in genetic programming
Extending genetic programming with recombinative guidance
Advances in genetic programming
Discovery of subroutines in genetic programming
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using genetic programming to approximate maximum clique
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Parsing and translation of expressions by genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Variations of the two-spiral task
Connection Science
Layered learning in boolean GP problems
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
Information Sciences: an International Journal
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Standard crossover in genetic programming (GP) selects two parents independently, based on fitness, and swaps randomly chosen portions of genetic material (subtrees). The mechanism by which the crossover operator achieves success in GP, and even whether crossover does in fact exhibit relative success compared to other operators such as mutation, is anything but clear [14]. An intuitive explanation for successful crossover would be that the operator produces fit offspring by combining the "strengths" of each parent. However, standard selection schemes choose each parent independently of the other, and with regard to overall fitness rather than more specific phenotypic traits. We present an algorithm for choosing parents which have complementary performance on a set of fitness cases, with an eye toward enabling the crossover operator to produce offspring which combine the distinct strengths of each parent. We test Complementary Phenotype Selection in three genetic programming domains: Boolean 6-Multiplexer, Intertwined Spirals Classification, and Sunspot Prediction. We demonstrate significant performance gains over the control methods in all of them and present a preliminary analysis of these results.