DOI: 10.5061/dryad.60hp6
title: Data from: The evolutionary origins of hierarchy

description: Hierarchical organization—the recursive composition of sub-modules—is
 ubiquitous in biological networks, including neural, metabolic,
 ecological, and genetic regulatory networks, and in human-made systems,
 such as large organizations and the Internet. To date, most research on
 hierarchy in networks has been limited to quantifying this property.
 However, an open, important question in evolutionary biology is why
 hierarchical organization evolves in the first place. It has recently been
 shown that modularity evolves because of the presence of a cost for
 network connections. Here we investigate whether such connection costs
 also tend to cause a hierarchical organization of such modules. In
 computational simulations, we find that networks without a connection cost
 do not evolve to be hierarchical, even when the task has a hierarchical
 structure. However, with a connection cost, networks evolve to be both
 modular and hierarchical, and these networks exhibit higher overall
 performance and evolvability (i.e. faster adaptation to new environments).
 Additional analyses confirm that hierarchy independently improves
 adaptability after controlling for modularity. Overall, our results
 suggest that the same force–the cost of connections–promotes the evolution
 of both hierarchy and modularity, and that these properties are important
 drivers of network performance and adaptability. In addition to shedding
 light on the emergence of hierarchy across the many domains in which it
 appears, these findings will also accelerate future research into evolving
 more complex, intelligent computational brains in the fields of artificial
 intelligence and robotics.
description: Adaptability Experiment
 DataNetwork information from
 the four adaptability experiments. There are a separate sub-folders for
 each of the adaptability experiment. A sub-folder has all networks evolved
 in the new environment, and for each network there is the individual.log
 file that contains the performance, modularity, hierarchy, and connection
 cost values.Adaptability_Experiment_Data.zipAND-EQU-AND Experiment DataAll the PA and P&CC networks from the AND-EQU-AND experiment are included. For both treatments, the data package contains network descritptions (in XML format) at generations 0 and 2500. Futhermore, for each network, the individual.log file provides the performance, modularity, hierarchy, and connection cost values of that networks. The solvent.txt file indicate the percentage of sub-problems that the network solves.AndEqualsAnd_Experiment_Data.zipMain Experiment DataThe main experimental data contains all evovled networks from PA and P&CC treatments. Network description at the 0th and 2500th generations are provided in XML format. For both final PA and P&CC networks, the individual.log file contains the performance, modularity, hierarchy, and connection cost values, and solvent.txt file has the value for the percentage of sub-problem solved.AndXorAnd_Main_Experiment_Data.zipLong Range Connection Experiment DataAll the PA and the P&CC networks from the experiment that allows connections to skip layers are included. In XML format, we provide network descritptions at the 0th and 2500th generations.Long_Range_Connections_Experiment_Data.zipMap-elites Exeriment DataThis data package contains a data file containing the performance, modularity, hierarchy of networks evolved via the map-elites algorithm. The data was dipicted in Fig.7 in the main manuscript.Map_Elites_Experiment_Data.zipNon-Modular Experiment DataThe modular but, hierarchical networks from the Non-Modular experiment are included, in this data package. For each network, there are the 0th and 2500th generation XML files containing the are serialized version of the network. Network's performance, modularity, hierarchy, and connection cost are given in the individual.log file, and the percentage of sub-problems the network solves is in solvent.txt file.Non_Modular_But_Hierarchal_Experiment_Data.zipOR-XOR-EQU Experiment DataThis data package contains PA and P&CC networks from the OR-XOR-EQU experiment. The two XML files contain the serialization of each network at the 0th and 2500th generations. In the individual.log file, the performance, modularity, hierarchy, and connection cost of a network are provided. The percentage of sub-problems that a network solves is in solvent.txt file.OR_XOR_EQU_Experiment_Data.zipThree_Outputs_Problem_Experiment_DataNetwork data from the experiment that evovled networks to solve the three-output problem. These data contain XML serialized PA and P&CC networks at the 0th and 2500th generations.Two-Output Experiment DataNetwork data from the experiment that involved three outputs problem. These data contain XML serialized PA and P&CC networks at the 0th and 2500th generations.Two_Outputs_Problem_Experiment_Data.zipPython Plotting ScriptsPython written plotting scripts, which are used to generate the figures in the paper.Python_Plottng_Scripts.ziphttps://github.com/henokyen/EOHThe source code that we used to do our experiments can be found at: https://github.com/henokyen/EOH