Genetic Algorithms for Binary Quadratic Programming

In this paper, genetic algorithms for the un-constrained binary quadratic programming problem (BQP) are presented. It is shown that for small problems a simple genetic algorithm with uniform crossover is suucient to nd optimum or best-known solutions in short time, while for problems with a high number of variables (n 200) it is essential to incorporate local search to arrive at high-quality solutions. A hybrid genetic algorithm incorporating local search is tested on 40 problem instances of sizes containing between n = 200 and n = 2500. The results of the computer experiments show that the approach is comparable to alternative heuris-tics such as tabu search for small instances and superior to tabu search and simulated annealing for large instances. New best solutions could be found for 14 large problem instances.