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Title: A Comparison of Genetic Programming Variants for Hyper-Heuristics

Abstract

Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.

Authors:
 [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1177599
Report Number(s):
SAND2015-2326R
579486
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Harris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. United States: N. p., 2015. Web. doi:10.2172/1177599.
Harris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. United States. doi:10.2172/1177599.
Harris, Sean. Sun . "A Comparison of Genetic Programming Variants for Hyper-Heuristics". United States. doi:10.2172/1177599. https://www.osti.gov/servlets/purl/1177599.
@article{osti_1177599,
title = {A Comparison of Genetic Programming Variants for Hyper-Heuristics},
author = {Harris, Sean},
abstractNote = {Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.},
doi = {10.2172/1177599},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Mar 01 00:00:00 EST 2015},
month = {Sun Mar 01 00:00:00 EST 2015}
}

Technical Report:

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