Fuzzy Co-Evolutionary Genetic Algorithm and its Application in Clinical Nutrition Decision

Gao Ping Wang; Meng Zhang

doi:10.4028/www.scientific.net/AMM.220-223.2352

Paper Titles

Parallel Solutions for Node Connectivity Based Mesh Moving Methods on Different Parallel Architectures
p.2330

Research on Key Techniques of Virtual Arthroscopic Knee Surgery Training System
p.2338

The Technology Research Based on Least Square Method for Extracting Quadric Surface
p.2342

A Genetic Algorithm for the Optimization Problem of Highway Maintenance Equipment Arrangement
p.2346

Fuzzy Co-Evolutionary Genetic Algorithm and its Application in Clinical Nutrition Decision
p.2352

The Developing of Algorithm for Learning Programs in the Adaptive Learning System
p.2356

One-Way Function Construction Based on the MQ Problem and Logic Function
p.2360

A Model for E-Government Information Resource Integration Based on Ontology
p.2364

Design of Image Retrieval System Based on Speech Recognition
p.2371

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 220-223Fuzzy Co-Evolutionary Genetic Algorithm and its...

Fuzzy Co-Evolutionary Genetic Algorithm and its Application in Clinical Nutrition Decision

Abstract:

The fuzzy co-evolution genetic algorithm is applied to clinical nutrition decision-making optimization. By cooperative co-evolutionary algorithm, the decision problem of clinical nutrition is divided into two populations ,which is combined into a complete diet recipe. In this paper, different fuzzy-based definitions of optimality and dominated solution are introduced. The corresponding extension of Co-Evolutionary Genetic Algorithm, so-called Fuzzy Co-Evolution Genetic Algorithm (FCEGA), will be presented as well. To verify the usefulness of such an approach, the approach is tested on analytical test cases in order to show its validity . The solutions, provided by the proposed algorithm for the clinical nutrition diet model,are promising when compared with an existing well-known algorithm.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 220-223)

Pages:

2352-2355

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.220-223.2352

Citation:

Cite this paper

Online since:

November 2012

Authors:

Gao Ping Wang, Meng Zhang

Keywords:

Clinical Nutrition Decision, Co-Evolutionary Genetic Algorithm, Multiobjective Evolutionary Algorithms

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Potter M, de Jong K. Cooperative co-evolution: an architecture for evolving co adapted subcomponents.Evolutionary Computation , Vol. 8-1(2000) ,pp.1-29

DOI: 10.1162/106365600568086

Google Scholar

[2] K¨oppen, M., Vicente Garcia, R. A fuzzy scheme for the ranking of multivariate data and its application,In: Proceedings of the 2004 Annual Meeting of the NAFIPS,Ban,Alberta,Canada, NAFIPS (2004),p.140–145

DOI: 10.1109/nafips.2004.1336266

Google Scholar

[3] Cao X B,Liu J L,Wang X F. Research on multiobjective optimization based on ecological cooperation.Journal of Software, ,Vol. 12-4 (2000) ,pp.521-528

Google Scholar

[4] Zitzler E, Thiele L.,Multi-Objective evolutionary algorithms: A comparative case study and the strength pareto approach,IEEE Trans. on Evolutionary Computation, Vol. 3-4 (1999) ,pp.257-271

DOI: 10.1109/4235.797969

Google Scholar

[5] Zitzler, E., Laumanns, M., Thiele, L., SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK report no.103, Swiss Federal Institute of Technology, Zürich, Switzerland (2001)

Google Scholar