Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
An Analysis of Koza's Computational Effort Statistic for Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
What's AI Done for Me Lately? Genetic Programming's Human-Competitive Results
IEEE Intelligent Systems
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Strongly typed genetic programming
Evolutionary Computation
Why evolution is not a good paradigm for program induction: a critique of genetic programming
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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We extend genetic programming (GP) with a local memory and vectorization to evolve simple, perceptron-like programs capable of learning by error correction. The local memory allows for a scalar value or vector to be stored and manipulated within a local scope of GP tree. Vectorization consists in grouping input variables and processing them as vectors. We demonstrate these extensions, along with an island model, allow to evolve general perceptron-like programs, i.e. working for any number of inputs. This is unlike in standard GP, where inputs are represented explicitly as scalars, so that scaling up the problem would require to evolve a new solution. Moreover, we find vectorization allows to represent programs more compactly and facilitates the evolutionary search.