New Crossover Methods for Sequencing Problems

In this paper we present two new crossover operators that make use of macro-order information and neighborhood information in sequencing problems. None of them needs local information, thus making them usable for a wide area of applications, e.g., optimal variable orders for binary decision diagrams, scheduling problems, seriation in archeology. The experimental results are promising. Especially they show that macro-order and neighborhood information is very important. 1 Introduction Genetic Algorithms (GAs) are one of the stochastic search algorithms based on evolutionary and biological processes that enable organisms to adapt more to their environment over many generations. They are being successfully applied to problems in business, engineering and science [1]. They work on a set of possible solutions which is called the population. The most basic operations used by GAs are selection, crossover, and mutation. The selection operator identifies 1 The paper will appear in Proceeding..