Combining Machine Learning and Genetic Algorithms to Solve the Independent Tasks Scheduling Problem
Bernabè Dorronsoro, Frédéric Pinel · 2017
We propose a new accurate and fast memetic parallel optimization algorithm for the independent tasks scheduling problem. The new technique combines the Virtual Savant (VS) with a parallel genetic algorithm (called PA-CGA). VS is an optimization framework based on machine learning that learns from a reference set of (pseudo-)optimal solutions how to solve the problem, providing accurate results in extremely low run times. We propose in this work the use of VS to generate a highly accurate initial population for the PA-CGA. Results show how initializing the population with VS (we test two versions of VS, differing on its training process) significantly increases the accuracy of the PA-CGA, compared to two other population initialization techniques: random and using a state-of-the-art heuristic.