Computational experiment demonstrates improved resource allocation in distributed networks under fluctuating demand, indicating environmental challenges enhance evolutionary optimization.
A cell based optimization (CBO) algorithm is proposed which takes inspiration from the collective behaviour of cellular slime molds (Dictyostellium discoideum). Experiments with CBO are conducted to study the ability of simple cell-like agents to collectively manage resources across a distributed network. Cells, or agents, only have local information can signal, move, divide, and die. Heterogeneous populations of the cells are evolved using Cartesian genetic programming (CGP). Several experiments were carried out to examine the adaptation of cells to changing user demand patterns. CBO performance was compared using various methods to change demand. The experiments showed that populations consistently evolve to produce effective solutions. The populations produce better solutions when user demand patterns fluctuated over time instead of environments with static demand. This is a surprising result that shows that populations need to be challenged during the evolutionary process to produce good results.
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Rothermich et al. (2003) studied this question.
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