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Representations for Genetic and Evolutionary Algorithms

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One of the major challenges for researchers in the field of management science,
information systems, business informatics, and computer science is to develop
methods and tools that help organizations, such as companies or public institutions,
to fulfill their tasks efficiently. However, during the last decade,
the dynamics and size of tasks organizations are faced with has changed.
Firstly, production and service processes must be reorganized in shorter time
intervals and adapted dynamically to the varying demands of markets and
customers. Although there is continuous change, organizations must ensure
that the efficiency of their processes remains high. Therefore, optimization
techniques are necessary that help organizations to reorganize themselves, to
increase the performance of their processes, and to stay efficient. Secondly,
with increasing organization size the complexity of problems in the context
of production or service processes also increases. As a result, standard, traditional,
optimization techniques are often not able to solve these problems
of increased complexity with justifiable effort in an acceptable time period.
Therefore, to overcome these problems, and to develop systems that solve
these complex problems, researchers proposed using genetic and evolutionary
algorithms (GEAs). Using these nature-inspired search methods it is possible
to overcome some limitations of traditional optimization methods, and to increase
the number of solvable problems. The application of GEAs to many
optimization problems in organizations often results in good performance and
high quality solutions.

For successful and efficient use of GEAs, it is not enough to simply apply
standard GEAs. In addition, it is necessary to find a proper representation for
the problem and to develop appropriate search operators that fit well to the
properties of the representation. The representation must at least be able to
encode all possible solutions of an optimization problem, and genetic operators
such as crossover and mutation should be applicable to it.

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