Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Sporadic model building for efficiency enhancement of hierarchical BOA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Decision Support System for Stock Market Trading
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A new modular genetic programming for finding attractive technical patterns in stock markets
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Parallel CHC algorithm for solving dynamic traveling salesman problem using many-core GPU
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This paper presents two evolutionary algorithms, ECGA and BOA, applied to constructing stock market trading expertise, which is built on the basis of a set of specific trading rules analysing financial time series of recent price quotations. A few modifications of ECGA are proposed in order to reduce the computing time and make the algorithm applicable for real-time trading. In experiments carried out on real data from the Paris Stock Exchange, the algorithms were compared in terms of the efficiency in solving the optimization problem, in terms of the financial relevance of the investment strategies discovered as well as in terms of the computing time.