Abstract
Based on some phenomena from human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals will be ensured to survive for some generations in order for them to have time to show their good genes; new individuals and the old ones are balanced to have the advantages of both. In order to quantitatively analyze the selective pressure, the concept of selection degree and a simple linear control equation are introduced. We can maintain the diversity of the evolutionary population by controlling the value of the selection degree. Performance of aGA is further enhanced by incorporating local search strategies.
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Partially supported by a National Key Basic Research Project of China and by a USA NSF grant CCR-0201253.
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Zhao, X., Gao, XS. Affinity genetic algorithm. J Heuristics 13, 133–150 (2007). https://doi.org/10.1007/s10732-006-9005-z
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DOI: https://doi.org/10.1007/s10732-006-9005-z