Genetic Algorithms (GAs) have shown to fit the complex needs arisung from many tasks of academic optimization for almost 20 years.(...)

The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This...

In this paper we concentrate on Job Shop Scheduling as a representative of constrained combinatorial problems. We introduce a new permutation representation for this problem. Three crossover operators different in tending to preserve the relative order, the absolute order, and the position in...

Advanced modeling of control and optimization in management science often leads to a computational complexity which cannot be handled by traditional algorithms and computer systems. On this background the paper develops a general approach to combine the power of distribution and parallelism in...

A frequently observed difficulty in the application of genetic algorithms to the domain of optimization ariscs from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active...

The population of parallel genetic algorithms (PGAs) can easily be split up to match the needs of a coarse grained parallelism. A cluster of interconnected workstations, seen as an MIMD-architecture, is the chosen hardware to express this kind of parallelism. A PGA implementation, as any other...

Advanced modeling of control and optimization in management science applications often leads to a computational complexity which cannot be handled by traditional algorithms and computer systems. Since the late 60's this fact (mathematically going by the term of NP-completeness) has been known...

This article conducts a computational study for the Job Shop Scheduling Problem. The focus lies on the structure of the search space as it appears for local search.