Optimization plays a key role in modern manufacturing. The genetic algorithm library is a flexible and modular component that provides solutions for arbitrary optimization problems. Genetic algorithms are stochastic, parallel search algorithms based on the mechanics of natural selection, the process of evolution [89Gol,75Hol]. They were first investigated and introduced by John Holland in 1975. Due to the increasing computing power, genetic algorithms in the past years became a feasible and robust search mechanism for a wide range of optimization problems. Scheduling of manufacturing jobs is one particular field of application of the InFrame genetic algorithm library that is implemented in the InFrame Scheduler. In order to get a basic feeling how GAs work, we provide a sample implementation of the commonly known travelling salesman problem (TSP). Feel free to play around... Explanation: The Travelling Salesman Problem is a deceptively simple combinatorial problem. The salesman has to visit n cities. In one tour he visits each city just once (a boundary condition could be that he has to finish up where he started). The problem to solve: What is the order of cities that allows for minimum travel distance ? You will need a Java enabled browser to view this applet! [89Gol] D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989. [75Hol] J. Holland. Adaptation In Natural and Artificial Systems. The University of Michigan Press, Ann Arbour, 1975.