Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
An immunity approach to strategic behavioral control
Engineering Applications of Artificial Intelligence
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
Clonal selection with immune dominance and anergy based multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
Game Theoretical Approach for Reliable Enhanced Indexation
Decision Analysis
Stock index tracking by Pareto efficient genetic algorithm
Applied Soft Computing
| Hi-index | 12.05 |
Enhanced index tracking is a popular strategy in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. In this paper, we propose a multi-objective optimization scheme for the enhanced index tracking problem, which provides the framework of defining the objectives as both maximizing the degree of beating the benchmark index and minimizing the accumulated error of underperforming the benchmark. Transaction costs are limited in the constraints. An immunity-based multi-objective optimization algorithm is presented to search for the solution of the enhanced index tracking problem. Treatment of infeasibility and solution selection are also presented. Our proposed approach is implemented to five data sets drawn from major world markets. The computational results compared with other published results show that our method has superior performance.