New Generic Hybrids Based upon Genetic Algorithms

In this paper we propose some generic extensions to the general concept of a Genetic Algorithm. These biologically and sociologically inspired interrelated hybrids aim to make the algorithm more open for scalability on the one hand, and to retard premature convergence on the other hand without necessitating the development of new coding standards and operators for certain problems. Furthermore, the corresponding Genetic Algorithm is unrestrictedly included in all of the newly proposed hybrid variants under special parameter settings. The experimental part of the paper discusses the new algorithms for the Traveling Salesman Problem as a well documented instance of a multimodal combinatorial optimization problem achieving results which significantly outperform the results obtained with a conventional Genetic Algorithm using the same coding and operators.

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