Line-breeding schemes for combinatorial optimization

Abstract

Line-breeding is an interesting mating strategy adapted to genetic algorithms. Most experiments on line-breeding have used multimodal functions as a testbed. In this work, we focus on combinatorial optimization problems. We chose the multiple constrained knapsack problem as our testbed. Several line-breeding schemes are explored, from the naive version to more advanced versions with sharing and niching. A new mechanism, dynamic mutation, is also proposed. Under this mechanism, if two individuals are very close to each other, instead of mating, a self-fertilization (i.e., a special mutation) is applied. The experiments presented in this paper show that a line-breeding scheme which uses multiple distanced champions can achieve the best performance. The paper also shows that dynamic mutation can improve not only the quality of solutions but can also identify more local optima.