Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem

Λιαγκούρας, Κωνσταντίνος; Metaxiotis, Kostas

doi:10.1007/s10479-018-2876-1

Cited by 6 publications

(2 citation statements)

References 58 publications

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“…Liagkouras and Metaxiotis proposed an information based evolutionary algorithm (Info Based EA) to tackle a fuzzy portfolio model (real-world FTSE-100 dataset) [30]. An initial solution was randomly generated.…”

Section: D: Evolutionary Algorithmmentioning

confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.

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Section: D: Evolutionary Algorithmmentioning

confidence: 99%

“…Transaction consts and bankruptcy constraints were included in [47]. Turnover, floor and ceiling, and cardinality constraints were integrated to the problem [30]. Cardinality, contingent, and floor and ceiling constraints were included in the problem [31].…”

Section: Research Limitations (Challenges)mentioning

confidence: 99%