Biology – Quantitative Biology – Populations and Evolution
Scientific paper
2011-03-09
PLoS Computational Biology 7 (2001) e1002236
Biology
Quantitative Biology
Populations and Evolution
27 pages, 8 figures, one supplementary figure. Three supplementary video files available on request. Version commensurate with
Scientific paper
10.1371/journal.pcbi.1002236
One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.
Adami Christoph
Chaumont Nicolas
Edlund Jeffrey
Hintze Arend
Koch Christof
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