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The application of genetic algorithms (GAs) to many optimization problems inorganizations often results in good performance and high quality solutions. Forsuccessful and efficient use of GAs, it is not enough to simply apply simpleGAs (SGAs). In addition, it is necessary to find a proper representation forthe problem and to develop appropriate search operators that fit well to theproperties of the genotype encoding. The representation must at least be ableto encode all possible solutions of an optimization problem, and geneticoperators such as crossover and mutation should be applicable to it. In thispaper, serial alternation strategies between two codings are formulated in theframework of dynamic change of genotype encoding in GAs for functionoptimization. Likewise, a new variant of GAs for difficult optimizationproblems denoted {\it Split-and-Merge} GA (SM-GA) is developed using a parallelimplementation of an SGA and evolving a dynamic exchange of individualrepresentation in the context of Dual Coding concept. Numerical experimentsshow that the evolved SM-GA significantly outperforms an SGA with static singlecoding.

Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems