Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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Simple enhancements to the standard population operators of mutation and crossover, utilizing Abstract Expression Grammars, are investigated. In previous works, Abstract Expression Grammars have been used to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution, into a seamlessly unified approach to symbolic regression. In this work, the potential for Abstract Expression Grammars to have a direct impact on the classic Genetic Programming mutation and crossover operators is demonstrated. The features of abstract expression grammars are explored, details of abstract mutation and crossover are provided, and the beneficial effects of abstract mutation and crossover are tested with several published nonlinear regression problems.