Abstract
Macroevolutionary algorithm (MA) is a new approach to optimization problems based on extinction patterns in macroevolution. It is different from the traditional population-level evolutionary algorithms such as genetic algorithms. In MAs, evolves at the level of higher taxa is used as the underlying metaphor. It is inspired by the latest models about evolution at large scale-macroevolution, while the traditional evolutionary algorithms are inspired in natural selection of darwinian theory. The MA model exploits the presence of links between “species” that represent candidate solutions to the optimization problem. In this paper, a hybrid MA which combines simulated annealing is proposed to solve complicated multi-modal optimization problems. Numerical simulation results show the power of this hybrid algorithm.
Supported by SRF for ROCS, SEM; Qingdao NSF (03-2-jz-19); Shandong NSF (Y2002G01); Research Fund of Qingdao Univ. (200204); Research fund of School of Automation (0305).
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Zhang, J., Xu, J. (2005). A Hybrid Macroevolutionary Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_35
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DOI: https://doi.org/10.1007/11539902_35
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