scholarly journals Using stochastic optimization methods for stock selling decision making and option pricing: numerics and bias and variance dependent convergence rates

2007 ◽  
Vol 7 (2) ◽  
pp. 111-132
Author(s):  
J. Bao ◽  
A. Belu ◽  
Y. Gershon ◽  
Y.J. Liu ◽  
G. Yin ◽  
...  
Keyword(s):  
Decision Making ◽  
Option Pricing ◽  
Stochastic Optimization ◽  
Convergence Rates ◽  
Optimization Methods

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

The Use of Soft Computing for Optimization in Business, Economics, and Finance

2013 ◽  
pp. 41-86 ◽  
Author(s):  
Petr Dostál
Keyword(s):  
Decision Making ◽  
Soft Computing ◽  
Optimization Methods ◽  
Business Economics ◽  
Decision Making Processes

Optimization methods have had successful applications in business, economics, and finance. Nowadays the new theories of soft computing are used for these purposes. The applications in business, economics, and finance have specific features in comparison with others. The processes are focused on private corporate attempts at money making or decreasing expenses; therefore the details of applications, successful or not, are not published very often. The optimization methods help in decentralization of decision-making processes to be standardized, reproduced, and documented. The optimization plays very important roles especially in business because it helps to reduce costs that can lead to higher profits and to success in the competitive fight.

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