Title :
An Exponential Moving Average algorithm
Author :
Haynes, David ; Corns, Steven ; Venayagamoorthy, Ganesh K.
Author_Institution :
Syst. Design, Aclara, St. Louis, MO, USA
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;
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
DOI :
10.1109/CEC.2012.6252962
