Handbook of Grammatical Evolution

1. Front Matter

   Pages i-x

   PDF

2. Introduction to 20 Years of Grammatical Evolution

   • Conor Ryan, Michael O’Neill, JJ Collins

   Pages 1-21

3. Understanding Grammatical Evolution: Grammar Design

   • Miguel Nicolau, Alexandros Agapitos

   Pages 23-53

4. On the Non-uniform Redundancy of Representations for Grammatical Evolution: The Influence of Grammars

   • Dirk Schweim, Ann Thorhauer, Franz Rothlauf

   Pages 55-78

5. Mapping in Grammatical Evolution

   • David Fagan, Eoin Murphy

   Pages 79-108

6. Theory of Disruption in GE

   • Erik Hemberg

   Pages 109-135

7. Structured Grammatical Evolution: A Dynamic Approach

   • Nuno Lourenço, Filipe Assunção, Francisco B. Pereira, Ernesto Costa, Penousal Machado

   Pages 137-161

8. Geometric Semantic Grammatical Evolution

   • Alberto Moraglio, James McDermott, Michael O’Neill

   Pages 163-188

9. GE and Semantics

   • Marina de la Cruz Echeandía, Younis R. SH. Elhaddad, Suzan Awinat, Alfonso Ortega

   Pages 189-218

10. Multi- and Many-Threaded Heterogeneous Parallel Grammatical Evolution

    • Amanda Sabatini Dufek, Douglas Adriano Augusto, Helio José Corrêa Barbosa, Pedro Leite da Silva Dias

    Pages 219-244

11. Comparing Methods to Creating Constants in Grammatical Evolution

    • R. Muhammad Atif Azad, Conor Ryan

    Pages 245-262

12. Grammatical Evolution in Finance and Economics: A Survey

    • Anthony Brabazon

    Pages 263-288

13. Synthesis of Parallel Programs on Multi-Cores

    • Gopinath Chennupati, R. Muhammad Atif Azad, Conor Ryan, Stephan Eidenbenz, Nandakishore Santhi

    Pages 289-315

14. Design, Architecture, and Engineering with Grammatical Evolution

    • Michael Fenton, Jonathan Byrne, Erik Hemberg

    Pages 317-339

15. Grammatical Evolution and Creativity

    • Róisín Loughran

    Pages 341-366

16. Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution

    • J. Ignacio Hidalgo, J. Manuel Colmenar, J. Manuel Velasco, Gabriel Kronberger, Stephan M. Winkler, Oscar Garnica et al.

    Pages 367-393

17. Grammatical Evolution Strategies for Bioinformatics and Systems Genomics

    • Jason H. Moore, Moshe Sipper

    Pages 395-405

18. Grammatical Evolution with Coevolutionary Algorithms in Cyber Security

    • Erik Hemberg, Anthony Erb Lugo, Dennis Garcia, Una-May O’Reilly

    Pages 407-431

19. Evolving Behaviour Tree Structures Using Grammatical Evolution

    • Diego Perez-Liebana, Miguel Nicolau

    Pages 433-460

20. Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials

    • Gilyana Borlikova, Louis Smith, Michael Phillips, Michael O’Neill

    Pages 461-486

This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool.    Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics.   Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization.

The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE.  

The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. 

Topics include:

•         Grammar design

•         Bias in GE

•         Mapping in GE

•         Theory of disruption in GE

·               Structured GE

·               Geometric semantic GE

·               GE and semantics

·               Multi- and Many-core heterogeneous parallel GE

·               Comparing methods to creating constants in GE

·               Financial modelling with GE

·               Synthesis of parallel programs on multi-cores

·               Design, architecture and engineering with GE

·               Computational creativity and GE

·               GE in the prediction of glucose for diabetes

·               GE approaches to bioinformatics and system genomics

·               GE with coevolutionary algorithms in cybersecurity

·               Evolving behaviour trees with GE for platform games

·               Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials

• Grammatical evolution
• Machine learning
• Automatic programming
• Evolutionary computation
• Artificial life

• Department of Computer Science and Information Systems, University of Limerick, Castletroy, Ireland

  Conor Ryan, JJ Collins

• School of Business, University College Dublin, Dublin, Ireland

  Michael O'Neill

Conor Ryan is Associate Professor of Machine Learning at the University of Limerick where he is director of the Biocomputing and Developmental Systems Group. His background includes the development of Machine Learning algorithms and their application to industrial scale problems such as medicine and microelectronics, and he holds several patents in the area of non-volatile memory. He was previously a Fulbright Scholar in the Computer Science and Artificial Intelligence Lab at MIT in 2013 and is also CTO of software at NVMdurance, a company that uses Machine Learning to extend the endurance of Flash Memory.

 Michael O'Neill holds the ICON Chair of Business Analytics at University College Dublin, and is Associate Dean - Director of the UCD Michael Smurfit Graduate Business School. He is a founding Director of the UCD Natural Computing Research & Applications Group and has over 300 publications on genetic programming, natural computing and their application in areas such as telecommunications networks, creativity, design, engineering, business analytics and finance.  He has co-authored four monographs including Grammatical Evolution (2003),  Biologically Inspired Algorithms for Financial Modelling (2006), Foundations in Grammatical Evolution for Dynamic Environments (2009), and Natural Computing Algorithms (2015).

 J.J. Collins holds an MSc in Artificial Intelligence from Queen Mary University of London. He is lecturer in the department of Computer Science and Information Systems at the University of Limerick, and is currently working on a higher research degree in the area of Evolutionary Computation.  His background includes computer vision, robotic mapping and localisation, minimisation of perceptual aliasing in reinforcement learning agents, and synthesis of algorithms using evolutionary paradigms. He was a core contributor to the design of the first Masters in Artificial Intelligence in Ireland in 2017. For J.J., the allure of the field of artificial intelligence, and the evolutionary paradigm in particular, has grown stronger over the years.

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