Suppose I am looking at an optimization problem with a large number of interconnected constraints, but the solution is - in some regions - extremely volatile (With volatile I mean: small mutations might drastically change the evaluation of the proposed solution. Also note that not all changes are volatile in this way, changing other parts of the solution might result in much smaller changes in the value of the solution).In this case, is it still practical to try and find an optimal solution by using a genetic algorithm? I am worried that the potential volatility would more or less change the problem into almost a brute-force search, or are there ways around this?
Using Genetic Algorithms for volatile problems
optimization;linear programming;genetic algorithms;constraint programming;constraint satisfaction