Evolutionary Optimization for Decision Making under Uncertainty

TL;DR

This paper surveys the use of evolutionary optimization techniques to solve stochastic programming problems involving decision making under uncertainty, highlighting their effectiveness in both single-stage and multi-stage scenarios.

Contribution

It provides a comprehensive overview of evolutionary algorithms applied to stochastic programming, emphasizing their advantages over traditional methods.

Findings

Evolutionary algorithms effectively solve complex stochastic programming problems.

They are applicable to both single-stage and multi-stage decision problems.

The survey highlights recent advancements and future research directions.

Abstract

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.