Genetic programming II (videotape): the next generation
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ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
A multi-chromosome approach to standard and embedded cartesian genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Incorporating characteristics of human creativity into an evolutionary art algorithm
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Advanced techniques for the creation and propagation of modules in cartesian genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Incorporating characteristics of human creativity into an evolutionary art algorithm
Genetic Programming and Evolvable Machines
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Parallel evolution using multi-chromosome cartesian genetic programming
Genetic Programming and Evolvable Machines
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
GECCO 2011 tutorial: cartesian genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
GECCO 2012 tutorial: cartesian genetic programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial: cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Embedded Cartesian Genetic Programming (ECGP) is a form of Genetic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. The results are compared with Cartesian Genetic Programming (CGP) with and without PR and show that ECGP improves evolvability and also that PR improves the performance of both techniques by up to eight times on the digital multiplier problems tested.