NDLI: Automatic modularization by speciation

Content Provider	IEEE Xplore Digital Library
Author	Darwen, P. Xin Yao
Copyright Year	1996
Description	Author affiliation: Sch. of Comput. Sci., Australian Defence Force Acad., Canberra, ACT, Australia (Darwen, P.)
Abstract	Real-world problems are often too difficult to solve by a single monolithic system. There are many examples of natural and artificial systems which show that a modular approach can reduce the total complexity of the system while solving a difficult problem satisfactorily. The success of modular artificial neural networks in speech and image processing is a typical example. However, designing a modular system is a difficult task. It relies heavily on human experts and prior knowledge about the problem. There is no systematic and automatic way to form a modular system for a problem. This paper proposes a novel evolutionary learning approach to designing a modular system automatically, without human intervention. Our starting point is speciation, using a technique based on fitness sharing. While speciation in genetic algorithms is not new, no effort has been made towards using a speciated population as a complete modular system. We harness the specialized expertise in the species of an entire population, rather than a single individual, by introducing a gating algorithm. We demonstrate our approach to automatic modularization by improving co-evolutionary game learning, learning to play the iterated prisoner's dilemma. We review some problems of earlier co-evolutionary learning methods, and explain their poor generalization ability and sudden mass extinctions. The generalization ability of our approach is significantly better than past efforts. Using the specialized expertise of the entire speciated population though a gating algorithm, instead of the best individual, is the main contributor to this improvement.
Starting Page	88
Ending Page	93
File Size	698040
Page Count	6
File Format	PDF
ISBN	0780329023
DOI	10.1109/ICEC.1996.542339
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-05-20
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Humans Computational intelligence Computer science Educational institutions Australia Speech processing Image processing Genetic algorithms Machine learning algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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