An Exponential Moving Average algorithm

Author

Haynes, David ; Corns, Steven ; Venayagamoorthy, Ganesh K.

Author_Institution

Syst. Design, Aclara, St. Louis, MO, USA

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

Abstract

Techniques to reduce the search space when an optimizer seeks an optimal value are studied in this paper. A new mutation technique called the “Exponential Moving Average” algorithm (EMA) is introduced. The performance of EMA algorithms is compared to two other similar Computational Intelligence (CI) algorithms (an ordinary Evolutionary Algorithm (EA) and a “Mean-Variance Optimization” (MVO)) to solve a multi-dimensional problem which has a large search space. The classic Sudoku puzzle is chosen as the problem with a large search space.

Keywords

evolutionary computation; games of skill; search problems; CI algorithms; EMA algorithms; MVO; Sudoku puzzles; computational intelligence algorithms; evolutionary algorithms; exponential moving average algorithm; mean-variance optimization; multidimensional problem; mutation technique; optimal value; optimizer; ordinary evolutionary algorithm; search space; Cells (biology); Evolutionary computation; Indexes; Optimization; USA Councils; Computational Intelligence; Evolutionary Computation; Games; Mean-Variance Optimization; Sudoku;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2012 IEEE Congress on

Conference_Location

Brisbane, QLD

Print_ISBN

978-1-4673-1510-4

Electronic_ISBN

978-1-4673-1508-1

Type

conf

DOI

10.1109/CEC.2012.6252962

Filename

6252962