Genetic Programming Algorithms for Dynamic Environments

Applications of Evolutionary Computation - Lecture Notes in Computer Science ◽

10.1007/978-3-319-31153-1_19 ◽

2016 ◽

pp. 280-295 ◽

Cited By ~ 2

Author(s):

João Macedo ◽

Ernesto Costa ◽

Lino Marques

Keyword(s):

Genetic Programming ◽

Dynamic Environments ◽

Programming Algorithms

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Penalty Functions for Genetic Programming Algorithms

Computational Science and Its Applications - ICCSA 2011 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-21928-3_40 ◽

2011 ◽

pp. 550-562 ◽

Cited By ~ 4

Author(s):

José L. Montaña ◽

César L. Alonso ◽

Cruz Enrique Borges ◽

Javier de la Dehesa

Keyword(s):

Genetic Programming ◽

Penalty Functions ◽

Programming Algorithms

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Influence of Experiment Design in GPA Investigating with Respect to PRNGs

MENDEL ◽

10.13164/mendel.2018.2.069 ◽

2018 ◽

Vol 24 (2) ◽

Author(s):

Tomas Brandejsky

Keyword(s):

Genetic Programming ◽

Random Number ◽

Experiment Design ◽

Parallel Execution ◽

Random Number Generators ◽

Pseudo Random Number ◽

Programming Algorithms

This paper analyses the influence of experiment parameters onto the reliability of experiments with genetic programming algorithms. The paper is focused on the required number of experiments and especially on the influence of parallel execution which affect not only the order of thread execution but also behaviors of pseudo random number generators, which frequently do not respect recommendation of C++11 standard and are not implemented as thread safe. The observations and the effect of the suggested improvements are demonstrated on results of 720,000 experiments.

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An Application of Genetic Programming to Forecasting Foreign Exchange Rates

Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery ◽

10.4018/978-1-60566-705-8.ch002 ◽

2010 ◽

pp. 26-48

Author(s):

Muneer Buckley ◽

Zbigniew Michalewicz ◽

Ralf Zurbruegg

Keyword(s):

Exchange Rate ◽

Genetic Programming ◽

Exchange Rates ◽

Foreign Exchange ◽

Economic Model ◽

Dynamic Environments ◽

Foreign Exchange Rates ◽

Better Than ◽

Made In

There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested.

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