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Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization

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Abstract

This paper introduces a novel niching scheme called the q-nearest neighbors replacement (q-NNR) method in the framework of the steady-state GAs (SSGAs) for solving binary multimodal optimization problems. A detailed comparison of the main niching approaches are presented first. The niching paradigm and difference of the selection-recombination genetic algorithms (GAs) and the recombination-replacement SSGAs are discussed. Then the q-NNR is developed by adopting special replacement policies based on the SSGAs; a Boltzmann scheme for dynamically sizing the nearest neighbors set is designed to achieve a speed-up and control the proportion of individuals adapted to different niches. Finally, experiments are carried out on a set of test functions characterized by deception, epistasis, symmetry and multimodality. The results are satisfactory and illustrate the effectivity and efficiency of the proposed niching method.

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  1. School of Management, Tianjin University, Tianjin, 300072, People’s Republic of China

    Minqiang Li & Jisong Kou

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  1. Minqiang Li
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  2. Jisong Kou
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Correspondence to Minqiang Li.

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Li, M., Kou, J. Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization. J Heuristics 14, 243–270 (2008). https://doi.org/10.1007/s10732-007-9035-1

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  • DOI: https://doi.org/10.1007/s10732-007-9035-1

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