• Corpus ID: 15577789

Evolutionary Optimization for Decision Making under Uncertainty

  title={Evolutionary Optimization for Decision Making under Uncertainty},
  author={Ronald Hochreiter},
Optimizing decision problems under uncertainty can be done using a variety of solution methods. Soft computing and heuristic approaches tend to be powerful for solving such problems. In this overview article, we survey Evolutionary Optimization techniques to solve Stochastic Programming problems - both for the single-stage and multi-stage case. 

Figures from this paper

Constrained Portfolio Optimization in Liability-Driven Investing
This thesis forms and implements a multi-stage portfolio optimization model, and solves it using a genetic algorithm to estimate suitable parameters for the scenario generation, and to make sure that the problem is solved in a computationally efficient manner.
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
A genetic algorithm is proposed that aids in classifying these LiDAR resulting point clouds and thus make them usable for map generation and is compared to a traditional k-means clustering.


Hybrid search for cardinality constrained portfolio optimization
How a genetic algorithm approach added to a simulated annealing (SA) process offers a better alternative to find the mean variance frontier in the portfolio selection process is described.
Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization
A multi-stage financial asset management decision optimization model is presented to outline strategies to analyze the impact of various algorithmic scenario generation methodologies.
Evolutionary Multi-stage Financial Scenario Tree Generation
A new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented and numerical results and implementation details are presented.
Evolutionary Stochastic Portfolio Optimization
  • R. Hochreiter
  • Economics
    Natural Computing in Computational Finance
  • 2008
In this chapter, the concept of evolutionary stochastic portfolio optimization is discussed. Selected theory from the fields of Stochastic Programming, evolutionary computation, portfolio
Non-linear Stochastic Optimization Using Genetic Algorithm for Portfolio Selection
A non-linear stochastic optimization algorithm named SPGA is proposed to determine a profitable portfolio selection planning plan under risk to solve this large-scale portfolio selection optimization problem.
Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling
A problem-specific EA for process engineering task is designed, following the MBEA guidelines and minimal moves mutation, and is compared to a straightforward application of a canonical EA/MIP and to a monolithic mathematical programming algorithm.
Generating Scenario Trees for Multistage Decision Problems
This paper presents a method based on nonlinear programming that can be used to generate a limited number of discrete outcomes that satisfy specified statistical properties, and argues that what are the relevant properties, will be problem dependent.
Multiobjective financial portfolio design: a hybrid evolutionary approach
This work introduces a powerful hybrid multiobjective optimization approach that combines evolutionary computation with linear programming to simultaneously maximize these return measures, minimize these risk measures, and identify the efficient frontier of portfolios that satisfy all constraints.
Financial scenario generation for stochastic multi-stage decision processes as facility location problems
The problem of finding valuable scenario approximations can be viewed as the problem of optimally approximating a given distribution with some distance function and it is shown that for Lipschitz continuous cost/profit functions it is best to employ the Wasserstein distance.
A statistical selection mechanism of GA for stochastic programming problems
  • K. Tokoro
  • Computer Science
    Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
  • 2001
We propose a new genetic algorithm to solve complex stochastic programming problems, in which possible combinations of parameters are provided as scenarios. The algorithm finds a solution efficiently