On the Performance and Convergence Properties of Hybrid Intelligent Schemes: Application on Portfolio Optimization Domain

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{vassiliadis_performance_2011,

• author = "Vassilios Vassiliadis and Nikolaos Thomaidis and George Dounias",
• title = "On the {Performance} and {Convergence} {Properties} of {Hybrid} {Intelligent} {Schemes}: {Application} on {Portfolio} {Optimization} {Domain}",
• year = "2011",
• booktitle = "Applications of {Evolutionary} {Computation} {EVOFIN}-2011, Part 2",
• editor = "Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar",
• volume = "6625",
• series = "Lecture Notes in Computer Science",
• pages = "131--140",
• address = "Turin, Italy",
• month = apr,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, continuous ACO, portfolio optimization",
• isbn13 = "978-3-642-20520-0",
• URL = "https://link.springer.com/chapter/10.1007/978-3-642-20520-0_14",
• DOI = "doi:10.1007/978-3-642-20520-0_14",
• abstract = "Hybrid intelligent algorithms, especially those who combine nature-inspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in terms of performance and convergence ability in a complex financial problem. Particularly, both algorithms use a type of genetic algorithm for asset selection and they differ on the technique applied for weight optimization: the first hybrid uses a numerical function optimization method, while the second one uses a continuous ant colony optimization algorithm. Results indicate that there is great potential in combining characteristics of nature-inspired algorithms in order to solve NP-hard optimization problems.",

}

Genetic Programming entries for Vassilios Vassiliadis Nikolaos Thomaidis Georgios Dounias

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