Analytic solutions to differential equations under graph-based genetic programming

Authors:
Tom Seaton;Gavin Brown;Julian F. Miller
Affiliations:
Department of Electronics, University of York;School of Computer Science, University of Manchester;Department of Electronics, University of York
Venue:
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Year:
2010

Citing 7
Cited 5

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Differential Equations with Maple V with Cdrom

Differential Equations with Maple V with Cdrom
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming

Genetic Programming and Evolvable Machines
Cartesian Genetic Programming

Proceedings of the European Conference on Genetic Programming
Investigating the performance of module acquisition in cartesian genetic programming

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Solving differential equations with genetic programming

Genetic Programming and Evolvable Machines
Comparison of tree and graph encodings as function of problem complexity

Proceedings of the 9th annual conference on Genetic and evolutionary computation

Cartesian genetic programming

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
Solving differential equations with Fourier series and Evolution Strategies

Applied Soft Computing
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cartesian Genetic Programming (CGP) is applied to solving differential equations (DE). We illustrate that repeated elements in analytic solutions to DE can be exploited under GP. An analysis is carried out of the search space in tree and CGP frameworks, examining the complexity of different DE problems. Experimental results are provided against benchmark ordinary and partial differential equations. A system of ordinary differential equations (SODE) is solved using multiple outputs from a genome. We discuss best heuristics when generating DE solutions through evolutionary search.