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11

EUROGP
2000
Springer

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Seeding Genetic Programming Populations

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Seeding Genetic Programming Populations

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We show genetic programming (GP) populations can evolve under the influence of a Pareto multi-objective fitness and program size selection scheme, from "perfect" programs which match the training material to general solutions. The technique is demonstrated with programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin Breast Cancer) and an insurance customer profiling task (Benelearn99 data mining).

William B. Langdon, Peter Nordin

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EUROGP 2000 | Optimization | Pareto Multi-objective Fitness | Programmatic Image Compression | Size Selection Scheme |

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Added	24 Aug 2010
Updated	24 Aug 2010
Type	Conference
Year	2000
Where	EUROGP
Authors	William B. Langdon, Peter Nordin

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