Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Sub-machine-code genetic programming
Advances in genetic programming
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Proceedings of the European Conference on Genetic Programming
A data parallel approach to genetic programming using programmable graphics hardware
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fast genetic programming on GPUs
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Hardware accelerators for Cartesian genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Challenges of evolvable hardware: past, present and the path to a promising future
Genetic Programming and Evolvable Machines
A flexible on-chip evolution system implemented on a xilinx Virtex-II pro device
ICES'05 Proceedings of the 6th international conference on Evolvable Systems: from Biology to Hardware
Reducing wasted evaluations in cartesian genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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This paper describes an efficient acceleration technique designed to speedup the evaluation of candidate solutions in Cartesian Genetic Programming (CGP). The method is based on translation of the CGP phenotype to a binary machine code that is consequently executed. The key feature of the presented approach is that the introduction of the translation mechanism into common fitness evaluation procedure requires only marginal knowledge of target CPU instruction set. The proposed acceleration technique is evaluated using a symbolic regression problem in floating point domain. It is shown that for a cost of small changes in a common CGP implementation, a significant speedup can be obtained even on a common desktop CPU. The accelerated version of CGP implementation accompanied with performance analysis is available for free download from http://www.fit.vutbr.cz/˜vasicek/cgp