Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms).

1 Foundations.- An Introduction to Evolutionary Algorithms.- An Overview of Evolutionary Algorithms in Management Applications.- 2 Applications in Industry.- A Genetic Algorithm Applied to Resource Management in Production Systems.- A Case Study of Operational Just-In-Time Scheduling Using Genetic Algorithms.- An Evolutionary Algorithm for Discovering Manufacturing Control Strategies.- Determining the Optimal Network Partition and Kanban Allocation in JIT Production Lines.- On Using Penalty Functions and Multicriteria Optimisation Techniques in Facility Layout.- Tapping the Full Power of Genetic Algorithm through Suitable Representation and Local Optimization: Application to Bin Packing.- A Hybrid Genetic Algorithm for the Two-Dimensional Guillotine Cutting Problem.- 3 Applications in Trade.- Facility Management of Distribution Centres for Vegetables and Fruits.- Integrating Machine Learning and Simulated Breeding Techniques to Analyze the Characteristics of Consumer Goods.- Adaptive Behaviour in an Oligopoly.- Determining a Good Inventory Policy with a Genetic Algorithm.- 4 Applications in Financial Services.- Genetic Algorithms and the Management of Exchange Rate Risk.- Evolving Decision Support Models for Credit Control.- Genetic Classification Trees.- A Model of Stock Market Participants.- 5 Applications in Traffic Management.- Using Evolutionary Programming to Control Metering Rates on Freeway Ramps.- Application of Genetic Algorithms for Solving Problems Related to Free Routing for Aircraft.- Genetic Algorithm with Redundancies for the Vehicle Scheduling Problem.- 6 Planning in Education.- Course Scheduling by Genetic Algorithms.- About the Authors.