Since its popularization by Syswerda (1989), uniform crossover (UC) has become perhaps the most widely used crossover operator, despite the fact that arguments backing low-disruption operators are as old as the genetic algorithm (GA) itself. We question the usual arguments given in favor of UC, and argue that on problems with even a mild degree of difficulty, disruption is indeed harmful. We conduct experiments on a function which is a simple concatenation of several copies of a non-separable function, which makes it difficult for a simple hill climber. We find that in this setup, UC is outperformed by the standard 2-point crossover fitted with inversion. According to our experiments, on problems where applying a GA makes sense, UC appears to perform worse than the classic 2-point crossover fitted with inversion, and essentially the same as a simple copy-and-mutate operator.

An important class of computational problems are grouping problems, where the aim is to group together members of a set (i.e., find a good partition of the set). We show why both the standard and the ordering GAs fare poorly in this domain by pointing out their inherent difficulty to capture the regularities of the functional landscape of the grouping problems. We then propose a new encoding scheme and genetic operators adapted to these problems, yielding the Grouping Genetic Algorithm (GGA). We give an experimental comparison of the GGA with the other GAs applied to grouping problems, and we illustrate the approach with two more examples of important grouping problems successfully treated with the GGA: the problems of Bin Packing and Economies of Scale.

The authors present an efficient genetic algorithm for two NP-hard problems, the bin packing and the line balancing problems. They define the two problems precisely and specify a cost function suitable for the bin packing problem. It is shown that the classic genetic algorithm performs poorly on grouping problems and an encoding of solutions of fitting these problems is presented. Efficient crossover and mutation operators are introduced for bin packing. The modification necessary to fit these operators for line balancing is given. Results of performance tests on randomly generated data are included. The line balancing tests cover real-world problem sizes. The results and areas of further research are discussed.>

Genetic algorithms (GAs) constitute a technique that has been applied with advantage to a variety of combinatorial problems. This work shows how the GAs can be used to optimize the job shop problem with many tasks, many machines, and precedence constraints. The authors introduce the technique of GAs and then show what makes the treatment of the job shop scheduling difficult. They then present an encoding of the problem that overcomes these difficulties. The performance of the algorithm is demonstrated with examples of real-world size.>