NDLI: Protein structure prediction with co-evolving memetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Smith, J.E.
Copyright Year	2003
Description	Author affiliation: Fac. of Comput., Eng., & Math. Sci., West of England Univ., Bristol, UK (Smith, J.E.)
Abstract	We present a coevolutionary learning-optimisation approach to protein structure prediction which uses a memetic algorithm as its underlying search method. Instance-specific knowledge can be learned, stored and applied by the system in the form of a population of rules. These rules determine the neighbourhoods used by the local search process, which is applied to each member of the coevolving population of candidate solutions. A generic coevolutionary framework is proposed for this approach, and then the implementation of a simple self-adaptive instantiation is described. A rule defining the local search's move operator is encoded as a {condition : action} pair and added to the genotype of each individual. It is demonstrated that the action of mutation and crossover on the patterns encoded in these rules, coupled with the action of selection on the resultant phenotypes is sufficient to permit the discovery and propagation of knowledge about the instance being optimised. The algorithm is benchmarked against a simple genetic algorithm, a memetic algorithm using a fixed neighbourhood function, and a similar memetic algorithm which uses random (rather than evolved) rules and shows significant improvements in terms of the ability to locate optimum configurations using Dill's HP model. It is shown that this "metalearning" of problem features provides a means of creating highly scaleable algorithms.
Sponsorship	Air Force Iffice of Sci. Res. Asian Office of Aerospace Res and Development Army Res. Office - Far East CISCO Evoluationary Programming Soc. IEE Inst. of Engineers, Australia IEEE Neural Networks Soc. Univ. of New South Wales
Starting Page	2346
Ending Page	2353
File Size	601306
Page Count	8
File Format	PDF
ISBN	0780378040
DOI	10.1109/CEC.2003.1299381
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Sequences Protein engineering Search methods Biological system modeling Lattices Genetic mutations Design optimization Computational biology Databases
Content Type	Text
Resource Type	Article

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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	Mr. Siddhartha Mukherjee siddhartha@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
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