An Overview of a Synergetic Combination of Local Search with Evolutionary Learning to Solve Optimization Problems

Abstract

We describe a method for solving combinatorial optimization problem that combines best aspects of local search and genetic algorithms. We formulate combinatorial optimization problems as state space search problems. While local search methods, such as hill climbing, are computationally efficient, they suffers from local minima traps. Global search methods are guaranteed to find optimal solutions, but are not always feasible. We favor a polynomial time technique that delivers solutions closer to optimal by modifying the search space of the local search method. We demonstrate our strategy on a single-machine scheduling problem with two objective functions: (1) minimizing average job completion time, and (2) minimizing total tardiness. We apply the technique to optimally schedule the robot arm of an automated retrieval system. Obtaining optimal solutions to such scheduling problems is computationally intractable, but experimental results show our technique produces better solutions than those found by genetic algorithm with random key encoding.