Applying EGENET to solve continuous constrained optimization problems: a preliminary report

GENET and its extended model EGENET are artificial neural networks to efficiently solve finite constraint satisfaction problems such as the car-sequencing problems. Both models use the min-conflict heuristic to update every finite-domain variable for finding local minima, and then apply heuristic learning rule(s) to escape the local minima not representing solution(s). Since continuous and finite domains are completely different, researchers seldom considered to apply the EGENET approach to solve continuous constrained optimization problems. We consider an interesting proposal to modify the original EGENET model with the minimal effort for solving continuous constrained optimization problems. Our proposal immediately opens up new directions for studying many possible ways to approximate continuous domains using modified finite-domain solvers. Moreover, the preliminary benchmarks of our prototypes on some graph layout problems as practical examples demonstrated some advantages of our proposal which prompts for further investigation.