research-article

Why evolution is not a good paradigm for program induction: a critique of genetic programming

Authors:

John R. Woodward,

Ruibin BaiAuthors Info & Claims

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

Pages 593 - 600

https://doi.org/10.1145/1543834.1543915

Published: 12 June 2009 Publication History

Abstract

We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an effective transmitter of information and crossover is an effective way to search through the viable combinations. Evolution is not without its limitations, which are pointed out, and it appears to be a highly effective problem solver; however we over-estimate the problem solving ability of evolution, as it is often trying to solve "self-imposed" survival problems. We are concerned with the evolution of Turing Equivalent programs (i.e. those with iteration and memory). Each of the mechanisms which make evolution work so well are examined from the perspective of program induction. Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated effectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.

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Index Terms

Why evolution is not a good paradigm for program induction: a critique of genetic programming
1. General and reference
2. Theory of computation

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cover image ACM Conferences

GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation

June 2009

1112 pages

ISBN:9781605583266

DOI:10.1145/1543834

General Chairs:
Lihong Xu
Tongji University, China
,
Erik D. Goodman
Michigan State University, USA
,
Program Chairs:
Guoliang Chen
University of Sci. & Tech. of China
,
Darrell Whitley
Colorado State University, USA
,
Yongsheng Ding
Donghua University, China

Copyright © 2009 ACM.

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Published: 12 June 2009

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genetic programming

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GEC '09

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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit

June 12 - 14, 2009

Shanghai, China

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Cited By

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https://doi.org/10.3390/sym16091146
Tauritz DWoodward JLi XHandl J(2024)Generative Hyper-heuristicsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648417(1069-1095)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3648417
Tauritz DWoodward JSilva SPaquete L(2023)Generative Hyper-heuristicsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595033(1069-1098)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595033
Tauritz DWoodward JWagner M(2022)Generative hyper-heuristicsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533646(1111-1140)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533646
Tauritz DWoodward JKrawiec K(2021)Hyper-heuristics tutorialProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461418(528-557)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461418
Tauritz DWoodward JCoello Coello C(2020)Hyper-heuristics tutorialProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389855(652-681)Online publication date: 8-Jul-2020
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Richter SSchoen MTauritz DAuger AStützle T(2019)Evolving mean-update selection methods for CMA-ESProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326827(1513-1517)Online publication date: 13-Jul-2019
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Tauritz DWoodward JAuger AStützle T(2019)Hyper-heuristics tutorialProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323382(770-805)Online publication date: 13-Jul-2019
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Krawiec KSimons CSwan JWoodward J(2018)Metaheuristic Design PatternsHandbook of Research on Emergent Applications of Optimization Algorithms10.4018/978-1-5225-2990-3.ch001(1-36)Online publication date: 2018
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Richter STauritz DTakadama K(2018)The automated design of probabilistic selection methods for evolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208304(1545-1552)Online publication date: 6-Jul-2018
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