Evolving exact integer algorithms with Genetic Programming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Evolving exact integer algorithms with Genetic Programming. / Weise, Thomas; Wan, Mingxu; Tang, Ke; Yao, Xin.

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 1816-1823 6900292.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Weise, T, Wan, M, Tang, K & Yao, X 2014, Evolving exact integer algorithms with Genetic Programming. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900292, Institute of Electrical and Electronics Engineers (IEEE), pp. 1816-1823, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900292

APA

Weise, T., Wan, M., Tang, K., & Yao, X. (2014). Evolving exact integer algorithms with Genetic Programming. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1816-1823). [6900292] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC.2014.6900292

Vancouver

Weise T, Wan M, Tang K, Yao X. Evolving exact integer algorithms with Genetic Programming. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE). 2014. p. 1816-1823. 6900292 https://doi.org/10.1109/CEC.2014.6900292

Author

Weise, Thomas ; Wan, Mingxu ; Tang, Ke ; Yao, Xin. / Evolving exact integer algorithms with Genetic Programming. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers (IEEE), 2014. pp. 1816-1823

Bibtex

@inproceedings{fdb1d0837432457b974c6911e6dd1946,
title = "Evolving exact integer algorithms with Genetic Programming",
abstract = "The synthesis of exact integer algorithms is a hard task for Genetic Programming (GP), as it exhibits epistasis and deceptiveness. Most existing studies in this domain only target few and simple problems or test a small set of different representations. In this paper, we present the (to the best of our knowledge) largest study on this domain to date. We first propose a novel benchmark suite of 20 non-trivial problems with a variety of different features. We then test two approaches to reduce the impact of the negative features: (a) a new nested form of Transactional Memory (TM) to reduce epistatic effects by allowing instructions in the program code to be permutated with less impact on the program behavior and (b) our recently published Frequency Fitness Assignment method (FFA) to reduce the chance of premature convergence on deceptive problems. In a full-factorial experiment with six different loop instructions, TM, and FFA, we find that GP is able to solve all benchmark problems, although not all of them with a high success rate. Several interesting algorithms are discovered. FFA has a tremendous positive impact while TM turns out not to be useful.",
author = "Thomas Weise and Mingxu Wan and Ke Tang and Xin Yao",
year = "2014",
month = sep,
day = "16",
doi = "10.1109/CEC.2014.6900292",
language = "English",
isbn = "9781479914883",
pages = "1816--1823",
booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

RIS

TY - GEN

T1 - Evolving exact integer algorithms with Genetic Programming

AU - Weise, Thomas

AU - Wan, Mingxu

AU - Tang, Ke

AU - Yao, Xin

PY - 2014/9/16

Y1 - 2014/9/16

N2 - The synthesis of exact integer algorithms is a hard task for Genetic Programming (GP), as it exhibits epistasis and deceptiveness. Most existing studies in this domain only target few and simple problems or test a small set of different representations. In this paper, we present the (to the best of our knowledge) largest study on this domain to date. We first propose a novel benchmark suite of 20 non-trivial problems with a variety of different features. We then test two approaches to reduce the impact of the negative features: (a) a new nested form of Transactional Memory (TM) to reduce epistatic effects by allowing instructions in the program code to be permutated with less impact on the program behavior and (b) our recently published Frequency Fitness Assignment method (FFA) to reduce the chance of premature convergence on deceptive problems. In a full-factorial experiment with six different loop instructions, TM, and FFA, we find that GP is able to solve all benchmark problems, although not all of them with a high success rate. Several interesting algorithms are discovered. FFA has a tremendous positive impact while TM turns out not to be useful.

AB - The synthesis of exact integer algorithms is a hard task for Genetic Programming (GP), as it exhibits epistasis and deceptiveness. Most existing studies in this domain only target few and simple problems or test a small set of different representations. In this paper, we present the (to the best of our knowledge) largest study on this domain to date. We first propose a novel benchmark suite of 20 non-trivial problems with a variety of different features. We then test two approaches to reduce the impact of the negative features: (a) a new nested form of Transactional Memory (TM) to reduce epistatic effects by allowing instructions in the program code to be permutated with less impact on the program behavior and (b) our recently published Frequency Fitness Assignment method (FFA) to reduce the chance of premature convergence on deceptive problems. In a full-factorial experiment with six different loop instructions, TM, and FFA, we find that GP is able to solve all benchmark problems, although not all of them with a high success rate. Several interesting algorithms are discovered. FFA has a tremendous positive impact while TM turns out not to be useful.

UR - http://www.scopus.com/inward/record.url?scp=84908584370&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900292

DO - 10.1109/CEC.2014.6900292

M3 - Conference contribution

AN - SCOPUS:84908584370

SN - 9781479914883

SP - 1816

EP - 1823

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -