Model approach to grammatical evolution: theory and case study

被引:27
|
作者
He, Pei [1 ,2 ,3 ]
Deng, Zelin [3 ]
Wang, Houfeng [4 ]
Liu, Zhusong [5 ]
机构
[1] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China
[2] Peking Univ, Minist Educ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China
[3] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Hunan, Peoples R China
[4] Peking Univ, Inst Computat Linguist, Beijing 100080, Peoples R China
[5] Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Genetic programming; Grammatical evolution; Finite state automaton; Model;
D O I
10.1007/s00500-015-1710-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.
引用
收藏
页码:3537 / 3548
页数:12
相关论文
共 50 条
  • [1] Model approach to grammatical evolution: theory and case study
    Pei He
    Zelin Deng
    Houfeng Wang
    Zhusong Liu
    Soft Computing, 2016, 20 : 3537 - 3548
  • [2] Analyzing Grammatical Evolution and πGrammatical Evolution with Grammar Model
    He, Pei
    Deng, Zelin
    Gao, Chongzhi
    Chang, Liang
    Hu, Achun
    INFORMATION TECHNOLOGY AND INTELLIGENT TRANSPORTATION SYSTEMS, VOL 2, 2017, 455 : 483 - 489
  • [3] Model approach to grammatical evolution: deep-structured analyzing of model and representation
    Pei He
    Zelin Deng
    Chongzhi Gao
    Xiuni Wang
    Jin Li
    Soft Computing, 2017, 21 : 5413 - 5423
  • [4] Model approach to grammatical evolution: deep-structured analyzing of model and representation
    He, Pei
    Deng, Zelin
    Gao, Chongzhi
    Wang, Xiuni
    Li, Jin
    SOFT COMPUTING, 2017, 21 (18) : 5413 - 5423
  • [5] Search, Neutral Evolution, and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution
    Wilson, Dominic
    Kaur, Devinder
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (03) : 566 - 590
  • [6] The case for Grammatical Evolution in test generation
    Murphy, Aidan
    Laurent, Thomas
    Ventresque, Anthony
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 1946 - 1947
  • [7] Areal pressure in grammatical evolution An Indo-European case study
    Cathcart, Chundra
    Carling, Gerd
    Larsson, Filip
    Johansson, Niklas
    Round, Erich
    DIACHRONICA, 2018, 35 (01) : 1 - 34
  • [8] A Case Study on Grammatical-Based Representation for Regular Expression Evolution
    Gonzalez-Pardo, Antonio
    Barrero, David F.
    Camacho, David
    R-Moreno, Maria D.
    TRENDS IN PRACTICAL APPLICATIONS OF AGENTS AND MULTIAGENT SYSTEMS, 2010, 71 : 379 - +
  • [9] OLD ENGLISH CASE AND GRAMMATICAL THEORY
    PENHALLURICK, JM
    LINGUA, 1975, 36 (01) : 1 - 29
  • [10] A Memetic Fuzzy ARTMAP by a Grammatical Evolution Approach
    Tan, Shing Chiang
    Lim, Chee Peng
    Watada, Junzo
    INTELLIGENT DECISION TECHNOLOGIES 2016, PT I, 2016, 56 : 447 - 456