.png){kind=icon}
Return
Learning genetic algorithm parameters using hidden Markov models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2005.04.045.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Genetic algorithms (GAs) are routinely used to search problem spaces of interest. A lesser known but growing group of applications of GAs is the modeling of so-called evolutionary processes, for example, organizational learning and group decision-making. Given such an application, we show it is possible to compute the likely GA parameter settings given observed populations of such an evolutionary process. We examine the parameter estimation process using estimation procedures for learning hidden Markov models, with mathematical models that exactly capture expected GA behavior. We then explore the sampling distributions relevant to this estimation problem using an experimental approach. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
artificial intelligence
evolutionary computations
evolutionary process
adaptive agents
genetic algorithms
heuristics
Markov processes
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
No organization information available
Cited Papers
A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSave
Efficient anomaly detection by modeling privilege flows using hidden Markov modelCOMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
ShareSaveBeyond Cyber-Doom: Assessing the Limits of Hypothetical Scenarios in the Framing of Cyber-Threats
PREAI
ShareSaveShareSaveShareSave
View more