Abstract
Allostasis is a biological term recently coined which explains how the modifications of specialized organ conditions inside the body allow to achieve stability when an unbalance condition is presented. In this chapter, a biologically-inspired algorithm, namely Allostatic Optimization (AO) is proposed for solving optimization tasks. The AO algorithm is based on the simulation of the allostasis mechanism. In AO, the searcher agents emulate different body conditions which interact to each other by using operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
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Cuevas, E., Díaz Cortés, M.A., Oliva Navarro, D.A. (2016). An Evolutionary Computation Algorithm based on the Allostatic Optimization. In: Advances of Evolutionary Computation: Methods and Operators. Studies in Computational Intelligence, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-28503-0_5
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