An Evolutionary Approach to Load Balancing Parallel Computations

We present a new approach to balancing the work load in a multicomputer when the problem is de composed into subproblems mapped to the processors. It is based on a hybrid genetic algo rithm. A number of design choices for genetic algo rithms are combined in order to ameliorate the problem of premature convergence that is often en countered in the implementation of classical genet ic algorithms. The algorithm is hybridized by including a hill climbing procedure which signifi cantly improves the efficiency of the evolution. Moreover, it makes use of problem specific infor mation to evade some computational costs and to reinforce favorable aspects of the genetic search at some appropriate points. The experimental results show that the hybrid genetic algorithm can find so lutions within 3% of the optimum in a reasonable time. They also suggest that this approach is not bi ased towards particular problem structures.