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Traditional Techniques of Genetic Algorithms Applied to Floating-Point Chromosome Representations

The choice of chromosome representation in genetic algorithms depends on the variables of the optimization problem being solved. If the variables are real-valued, the chromosomes can be represented as fixed-point integer values which enables the use of classical genetic operators defined for binary strings. Another possible chromosome representation is using floating-point numbers directly. In this case, genetic operators have to be defined additionaly. This paper presents a different approach: floating-point chromosome representation with traditional genetic operators. In order to achieve fine local tuning of the solutions, a mapping is defined which dynamically changes the operating scope of genetic operators.

genetic algorithms; floating-point chromosome representation; reducing operating scope; fine local tuning

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