A fuzzy GGA-based approach to speed up the evolutionary process for diverse group stock portfolio optimization1

Author:

Chen Chun-Hao1,Chiang Bing-Yang2,Hong Tzung-Pei23,Wang Ding-Chau4,Lin Jerry Chun-Wei5,Gankhuyag Munkhjargal1

Affiliation:

1. Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan

2. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

4. Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan

5. Department of Computing, Mathematics, and Physics, Western Norway University of Computer Sciences, Bergen, Norway

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference42 articles.

1. A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers;Amador-Angulo;Soft Computing,2018

2. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems;Brest;IEEE Transactions on Evolutionary Computation,2006

3. A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot;Castillo;Applied Soft Computing,2015

4. W. Chen , D. Li and Y.J. Liu , A novel hybrid ICA-FA algorithm for multi-period uncertain portfolio optimization model based on multiple criteria, IEEE Transactions on Fuzzy Systems (2018), 1–1.

5. An approach for diverse group stock portfolio optimization using the fuzzy grouping genetic algorithm;Chen;Asian Conference on Intelligent Information and Database Systems,2018

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