Name
Abstract | ||
|---|---|---|
Guided Local Search is a powerful meta-heuristic algorithm that has been applied to a successful Genetic Programming Financial Forecasting tool called EDDIE. Although previous research has shown that it has significantly improved the performance of EDDIE, it also increased its computational cost to a high extent. This paper presents an attempt to deal with this issue by combining Guided Local Search with Fast Local Search, an algorithm that has shown in the past to be able to significantly reduce the computational cost of Guided Local Search. Results show that EDDIE's computational cost has been reduced by an impressive 77%, while at the same time there is no cost to the predictive performance of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIFEr.2014.6924091 | CIFEr |
Keywords | Field | DocType |
economic forecasting,metaheuristic algorithm,genetic programming financial forecasting tool,computational cost,forecasting theory,search problems,guided fast local search,finance,genetic algorithms,eddie,prediction algorithms,algorithm design and analysis,forecasting,radio frequency,mathematical model | Financial forecasting,Algorithm design,Guided Local Search,Computer science,Algorithm,Genetic programming,Prediction algorithms,Artificial intelligence,Local search (optimization),Machine learning | Conference |
ISSN | Citations | PageRank |
2380-8454 | 2 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Shao | 1 | 2 | 0.36 |
| Dafni Smonou | 2 | 2 | 0.36 |
| Michael Kampouridis | 3 | 94 | 16.60 |
| Edward P. K. Tsang | 4 | 899 | 87.77 |