Abstract
We propose a novel genetic algorithm which we call a parameter-free genetic algorithm (PfGA). The PfGA is inspired by the idea of a biological evolution hypothesis, i.e., the “disparity theory of evolution.” The theory is based on different mutation rates in double strands of DNA. Its idea can be extended to a very compact and fast adaptive search algorithm accelerating its evolution based on the variable size of a population and taking a dynamic but delicate balance between exploration (i.e., global search) and exploitation (i.e., local search). The PfGA is not only simple and robust, but it is unnecessary to set almost all the genetic parameters in advance which need to be set up in other genetic algorithms. Furthermore, a uniformly distributed parallel architecture and a master-slave architecture for the PfGA are investigated as an extension. We discuss the performance of the parallel distributed architectures using a general set of function optimization problems including the functions in the first Internatinal Contest on Evolutionary Optimization. On the other hand, gene duplication theory was first proposed by a Japanese biologist, Dr. Susumu Ohno, in the 1970’s. Inspired by this theory, we develop a gene-duplicating genetic algorithm. Several variants of this algorithm are considered. Individuals with various lengths of genes are evolved based on the PfGA or steady-state GA and then genes with different lengths are concatenated by migrating among subpopulations. To verify the effectiveness of the gene-duplicating genetic algorithm, we also performed a comparative study using the general set of function optimization problems.
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Sawai, H., Adachi, S., Kizu, S. (2003). New Schemes of Biologically Inspired Evolutionary Computation. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_4
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DOI: https://doi.org/10.1007/978-3-642-18965-4_4
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