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. © 2012 IEEE.
Recommended Citation
D. Haynes et al., "An Exponential Moving Average Algorithm," 2012 IEEE Congress on Evolutionary Computation, CEC 2012, article no. 6252962, Institute of Electrical and Electronics Engineers, Oct 2012.
The definitive version is available at https://doi.org/10.1109/CEC.2012.6252962
Department(s)
Engineering Management and Systems Engineering
Second Department
Electrical and Computer Engineering
Keywords and Phrases
Computational Intelligence; Evolutionary Computation; Games; Mean-Variance Optimization; Sudoku
International Standard Book Number (ISBN)
978-146731509-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
04 Oct 2012
Included in
Electrical and Computer Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
