A Genetic Algorithm for the Structural Estimation of Games with Multiple Equilibria

2005 ◽  
Author(s):  
Victor Aguirregabiria ◽  
Pedro Mira
Keyword(s):  
Genetic Algorithm ◽  
Multiple Equilibria ◽  
Structural Estimation

A HYBRID GENETIC ALGORITHM FOR THE MAXIMUM LIKELIHOOD ESTIMATION OF MODELS WITH MULTIPLE EQUILIBRIA: A FIRST REPORT

2005 ◽  
Vol 01 (02) ◽  
pp. 295-303 ◽  
Author(s):  
VICTOR AGUIRREGABIRIA ◽  
PEDRO MIRA
Keyword(s):  
Genetic Algorithm ◽  
Maximum Likelihood ◽  
Multiple Equilibria ◽  
Structural Parameters ◽  
Hybrid Genetic Algorithm ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Large Space ◽  
Pseudo Maximum Likelihood ◽  
Structural Econometric

This paper presents a hybrid genetic algorithm to obtain maximum likelihood estimates of parameters in structural econometric models with multiple equilibria. The algorithm combines a pseudo maximum likelihood (PML) procedure with a genetic algorithm (GA). The GA searches globally over the large space of possible combinations of multiple equilibria in the data. The PML procedure avoids the computation of all the equilibria associated with every trial value of the structural parameters.

Repeated-Evaluation Genetic Algorithm for Simulating Social Security in the Artificial Society

2011 ◽  
Vol 103 ◽  
pp. 513-517
Author(s):  
Zi Qing Ye ◽  
Xiao Yi Yu
Keyword(s):  
Genetic Algorithm ◽  
Social Interactions ◽  
Multiple Equilibria ◽  
Social Cost ◽  
Artificial Agents ◽  
Mathematical Form ◽  
Optimal Solutions ◽  
Artificial Society ◽  
The Social ◽  
Repeated Evaluation

In order to find optimal policy to govern agents’ society, artificial agents have been deployed in simulating social or economic phenomena. However, with an increase of the complexity of agents’ internal behaviors as well as their social interactions, modeling social behaviors and tracking down optimal policies in mathematical form become intractable. In this paper, the repeated evaluation genetic algorithm is used to find optimal solutions to deter criminals in order to reduce the social cost caused by the crimes in the artificial society. The society is characterized by multiple equilibria and noisy parameters. Sampling evaluation is used to evaluate every candidate. The results of experiments show that genetic algorithms can quickly find the optimal solutions.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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