2006Journal of Northeastern UniversityRequires access

A Hybrid Algorithm Based on Genetic Algorithm and Tabu Search to Solve Portfolio Problem

Pan De-hui

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Abstract

A hybrid algorithm is proposed on the basis of genetic algorithm and tabu search,where the main frame of the algorithm is provided by genetic algorithm and the tabu search is taken as the mutation operator.Here the tabu search is used for the solution space in the process of mutation.The hybrid algorithm is devised to solve the portfolio investment model in terms of probability criterion.An example shows that the hybrid algorithm is superior to the early genetic algorithm in mountain-climbing ability and computing efficiency.So,the hybrid algorithm will provide an effective way in making decision to investors.

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What this paper is about

A hybrid algorithm is proposed on the basis of genetic algorithm and tabu search,where the main frame of the algorithm is provided by genetic algorithm and the tabu search is taken as the mutation operator.Here the tabu search is used for the solution space in the process of mutation.The hybrid algorithm is devised to solve the portfolio investment model in terms of probability criterion.An example shows that the hybrid algorithm is superior to the early genetic algorithm in mountain-climbing ability and computing efficiency.So,the hybrid algorithm will provide an effective way in making decision to investors.

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Available abstract

A hybrid algorithm is proposed on the basis of genetic algorithm and tabu search,where the main frame of the algorithm is provided by genetic algorithm and the tabu search is taken as the mutation operator.Here the tabu search is used for the solution space in the process of mutation.The hybrid algorithm is devised to solve the portfolio investment model in terms of probability criterion.An example shows that the hybrid algorithm is superior to the early genetic algorithm in mountain-climbing ability and computing efficiency.So,the hybrid algorithm will provide an effective way in making decision to investors.

Key concepts: Tabu search, Hill climbing, Algorithm, Genetic algorithm, Population-based incremental learning, Hybrid algorithm (constraint satisfaction), Mathematical optimization, Computer science

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