Introduction
Hierarchy is an important organizational property in many biological and manmade systems, ranging from neural [2, 3], ecological [4], metabolic [5], and genetic regulatory networks [6], to the organization of companies [7], cities [8], societies [9], and the Internet [10, 11]. There are many types of hierarchy [12, 13, 14], but the one most relevant for biological networks [15]
, especially neural networks
[2, 3, 16], refers to a recursive organization of modules [11, 14]. Modules are defined as highly connected clusters of entities that are only sparsely connected to entities in other clusters [17, 18, 19]. Such hierarchy has long been recognized as a ubiquitous and beneficial design principle of both natural and manmade systems [15]. For example, in complex biological systems, the hierarchical composition of modules is thought to confer greater robustness and adaptability [2, 3, 20, 16], whereas in engineered designs, a hierarchical organization of simple structures accelerates the design, production, and redesign of artifacts [18, 21, 22].While most studies of hierarchy focus on producing methods to quantify it [12, 5, 10, 23, 24, 25, 26, 27, 15], a few have instead examined why hierarchy emerges in various systems. In some domains, the emergence of hierarchy is well understood; e.g., in complex systems, such as social networks, ecosystems, and road networks, the emergence of hierarchy can be explained solely by local decisions and or interactions [28, 29, 30, 4]. But, in biological systems, where the evolution of hierarchy is shaped by natural selection, why hierarchy evolves, and whether its evolution is due to direct or indirect selection, is an open and interesting question [4, 31]. Nonadaptive theories state that the hierarchy in some, but not all, types of biological networks may emerge as a byproduct of random processes [28]. Most adaptive explanations claim that hierarchy is directly selected for because it confers evolvability [32], which is the ability of populations to quickly adapt to novel environments [33]. Yet in computational experiments that simulate natural evolution, hierarchy rarely, if ever, evolves on its own [34, 35, 36], suggesting that alternate explanations are required to explain the evolutionary origins of hierarchy. Moreover, even if hierarchy, once present, is directly selected for because of the evolvability it confers, explanations are still required for how that hierarchy emerges in the first place.
In this paper we investigate one such hypothesis: the existence of costs for network connections creates indirect selection for the evolution of hierarchy. This hypothesis is based on two lines of reasoning. The first is that hierarchy requires a recursive composition of modules [11], and the second is that hierarchy includes sparsity. A recent study demonstrated that both modularity and sparsity evolve because of the presence of a cost for network connections [17]. Connection costs may therefore promote both modularity and sparsity, and thus may also promote the evolution of hierarchy.
It is realistic to incorporate connection costs into biological network models because it is known that there are costs to create connections, maintain them, and transmit information along them [37, 38]. Additionally, evidence supports the existence of a selection pressure in biological networks to minimize the net cost of connections. For example, multiple studies have shown that biological neural networks, which are hierarchical [2, 3]
, have been organized to reduce their amount of wiring by having fewer long connections and by locating neurons optimally to reduce the wiring between them
[38, 39, 40, 41].A relationship between hierarchy and connection costs can also be observed in a variety of different manmade systems. For example, very large scale integrated circuits (VLSI), which are designed to minimize wiring, are hierarchically organized [16]. In organizations such as militaries and companies, a hierarchical communication model has been shown to be an ideal configuration when there is a cost for communication links between organization members [42]. However, there is no prior work that tests whether the presence of connection costs is responsible for the evolution of hierarchy. Here we test that hypothesis in computational simulations of evolution and our experiments confirm that hierarchy does indeed evolve when there is a cost for network connections (Fig. 1).
Values  

Input pattern  0 0 1 1 0 1 1 1 
AND gate  0 1 0 1 
XOR gate  1 1 
AND gate  1 
A). Networks receive 8bit vectors as inputs. As shown, a successful network could AND adjacent input pairs, XOR the resulting pairs, and AND the result. Performance is a function only of the final output, and thus does not depend on how the network solves the problem; Other, nonhierarchical solutions also exist.
We also investigate the hypothesis that hierarchy confers evolvability, which has long been argued [2, 3, 16, 43], but has not previously been extensively tested [16]. Our experiments confirm that hierarchical networks, evolved in response to connection costs, exhibit an enhanced ability to adapt.
Experimentally investigating the evolution of hierarchy in biological networks is impractical, because natural evolution is slow and it is not currently possible to vary the cost of biological connections. Therefore, we conduct experiments in computational simulations of evolving networks. Computational simulations of evolution have shed substantial light on open, important questions in evolutionary biology [44, 45, 46], including the evolution of modularity [17, 19, 47, 48, 49], a structural property closely related to hierarchy. In such simulations, randomly generated individuals recombine, mutate, and reproduce based on a fitness function that evaluates each individual according to how well they perform a task. The task can be analogous to efficiently metabolizing resources or performing a required behavior. This process of evolution cycles for a predetermined number of generations.
We evolved computational abstractions of animal brains called artificial neural networks (ANNs) [50, 51] to solve hierarchical Boolean logic problems (Fig. 2A). In addition to abstracting animal brains, ANNs have also been used as abstractions of gene regulatory networks [52]. They abstract both because they sense their environment through inputs and produce outputs, which can either be interpreted as regulating genes or moving muscles (Methods). In our experiments, we evolve the ANNs with or without a cost for network connections. Specifically, the experimental treatment selects for maximizing performance and minimizing connection costs (performance and connection cost, P&CC), whereas the control treatment selects for performance only (performance alone, PA).
In all treatments the evolving networks have eight inputs and a single output. During evaluation, each network is tested on all possible (256) input patterns of zeros and ones, and the network’s output is checked against a hierarchical Boolean logic function provided with the same input (Fig. 2A and Table 1). An ANN output is considered True and an output is considered False. A network’s performance (fitness) is its percent of correct answers over all input patterns.
Results
On the main experimental problem (Fig. 2A), the addition of a connection cost leads to the evolution of significantly more hierarchical networks (Fig. 2B,G). Confirming previous findings on different problems [17, 36], the addition of a connection cost also significantly increases modularity (Fig. 2C,G) and reduces the number of generations required to evolve a solution (Fig. 2D).
Importantly, while final performance levels for the performance and connection cost (P&CC) treatment are similar to those of the performance alone (PA) treatment, there is a qualitative difference in how the networks solve the problem. P&CC networks exhibit functional hierarchy in that they solve the overall problem by recursively combining solutions to subproblems (Fig. 2F), whereas the PA networks tend to combine all input information in the first layer and then process it in a monolithic fashion (Fig. 2E). Such functional hierarchy can be quantified as the percent of subproblems a network solves (e.g. the AND and XOR gates in Fig. 2A). A subproblem is considered solved if, for all possible network inputs, and for any threshold, a neuron in a network exists that outputs an abovethreshold value whenever the answer to the subproblem is True, and a subthreshold value when the answer is False, or viceversa (Methods). This measure reveals that evolved P&CC networks solve significantly more subproblems than their PA counterparts (Fig. 2G, via Fisher’s exact test).
To further investigate how the ability to solve subproblems is related to hierarchy and modularity, we plotted the percent of subproblems solved vs. both hierarchy and modularity (Fig. 3). The plots show a significant, strong, positive correlation between the ability to solve subproblems and both hierarchy and modularity.
It has been hypothesized that one advantage of network hierarchy is that it confers evolvablity [2, 3, 16]. We test this hypothesis by first evolving networks to solve one problem (the base environment) and then evolving those networks to solve a different problem (the target environment). To isolate evolvability, we keep initial performance equal by taking the first 30 runs of each treatment (PA and P&CC) that evolve a perfectlyperforming network for the base environment [17]. Each of these 30 networks then seeds 30 runs in the target environment (for 900 total replicates per treatment). The base and target problems are both hierarchical and share some, but not all, subproblems (Fig. 4). Evolution in the target environment continues until the new problem is solved or 25000 generations elapse. We quantify evolvability as the number of generations required to solve the target problem [17]. We performed three such experiments, each with different base and target problems. In all experiments, P&CC networks take significantly fewer generations to adapt to the new environment than PA networks. They also solve significantly more of the target problem’s subproblems (Fig. 4).
One possible reason for the fast adaptation of P&CC networks is that their modular structure allows solutions to subproblems to be reused in different contexts [17]. A hierarchical structure may also be beneficial if both problems are hierarchical, even if the computation at points in the structure is different. For example, modules that solve XOR gates can quickly be rewired to solve EQU gates (Fig. 4A). Another reason for faster P&CC adaptation could be that these networks are sparser, meaning that fewer connections need to be optimized.
To further understand why connection costs increase hierarchy, we generated 20000 random, valid networks for each number of connections a network could have (Methods). A network is valid if it has a path from each of the input nodes to the output node. The networks were neither evolved nor evaluated for performance. Of these networks, those that are lowcost tend to have high hierarchy, and those with a high cost have low hierarchy (Fig. 5, left). This inherent association between low connection costs and high hierarchy suggests why selecting for lowcost networks promotes the evolution of hierarchical networks. It also suggests why networks evolve to be nonhierarchical without a pressure to minimize connection costs. Indeed, most evolved PA networks reside in the highcost, lowhierarchy region, whereas all P&CC networks occupy the lowcost, highhierarchy region (Fig. 5 left).
Similarly, there is also an inverse relationship between cost and modularity for these random networks (Fig. 5, right), as was shown in [17]. All evolved P&CC networks are found in the lowcost, highmodularity region; PA networks are spread over the lowmodularity, highcost region (Fig. 5, right).
As Figs. 3 and 5 suggest, network modularity and hierarchy are highly correlated (Fig. S12, Pearson’s correlation coefficient , based on a ttest with a correlation of zero as the null hypothesis). It is thus unclear whether hierarchy evolves as a direct consequence of connection costs, or if its evolution is a byproduct of evolved modularity. To address this issue we ran an additional experiment where evolutionary fitness was a function of networks being lowcost (as in the P&CC treatment), highperforming (as in all treatments), and nonmodular (achieved by selecting for low modularity scores). We call this treatment P&CCNonMod. The results reveal that P&CCNonMod networks have the same low level of modularity as PA networks (Fig. 6AB, ), but have significantly higher hierarchy (Fig. 6A, C, ) and solve significantly more subproblems than PA networks (Fig. 6D). These results reveal that, independent of modularity, a connection cost promotes the evolution of hierarchy. Additionally, P&CCNonMod networks are significantly more evolvable than PA networks (Fig. 6EG), revealing that hierarchy promotes evolvability independently of the known evolvability improvements caused by modularity [17].
To gain better insight into the relationship between modularity, hierarchy, and performance, we searched for the highestperforming networks at all possible levels of modularity and hierarchy. We performed this search with the multidimensional archive of phenotypic elites (MAPElites) algorithm [53]. The results show that networks with the same modularity can have a wide range of different levels of hierarchy, and viceversa, which indicates that these network properties can vary independently (Fig. 7). Additionally, the highhierarchy, highmodularity region, in which evolved P&CC networks reside, contains more highperforming solutions than the lowhierarchy, lowmodularity region where PA networks reside (Fig. 7), suggesting an explanation for why P&CC networks find highperforming solutions faster (Fig. 2D).
To test the generality of our hypothesis that the addition of a connection cost promotes the evolution of hierarchy in addition to modularity, we repeated our experiments with different Booleanlogic problems. For all problem variants, P&CC networks are significantly more hierarchical, modular, and solve more subproblems than PA networks (Fig. 8). The P&CC treatment also evolved highperforming networks significantly faster on all of these additional problems (Fig. S13).
Discussion
The evolution of hierarchy is likely caused by multiple factors. These results suggest that one of those factors is indirect selection to reduce the net cost of network connections. Adding a cost for connections has previously been shown to evolve modularity [17, 36]; the results in this paper confirm that finding and further show that a cost for connections also leads to the evolution of hierarchy. Moreover, the hierarchy that evolves is functional, in that it involves solving a problem by recursively combining solutions to subproblems. It is likely that there are other forces that encourage the evolution of hierarchy, and that this connection cost force operates in conjunction with them; identifying other drivers of the evolution of hierarchy and their relative contributions is an interesting area for future research.
These results also reveal that, like modularity [48, 17], hierarchy improves evolvability. While modularity and hierarchy are correlated, the experiments we conducted where we explicitly select for networks that are hierarchical, but nonmodular, reveal that hierarchy improves evolvability even when modularity is discouraged.
An additional factor that is present in modular and hierarchal networks is sparsity, a term meaning that only a few connections exist of the total that could. It is possible that this property explains some or even all of the evolvability benefits of modular, hierarchical networks. Future work is needed to address the difficult challenge of experimentally teasing apart these related properties.
As has been pointed out for the evolution of modularity [17], even if hierarchy, once present, is directly selected for because it increases evolvability, that does not explain its evolutionary origins, because enough hierarchy has to be present in the first place before those evolvability gains can be selected for. This paper offers one explanation for how sufficient hierarchy can emerge in the first place to then provide evolvability (or other) benefits that can be selected for.
This paper shows the effect of a connection cost on the evolution of hierarchy via experiments on many variants of one class of problem (hierarchical logic problems with many inputs and one output). In future work it will be interesting to test the generality of these results across different classes of problems, including nonhierarchical problems. The data in this paper suggest that a connection cost will always make it more likely for hierarchy to evolve, but it remains an open, interesting question how wide a range of problems hierarchy will evolve on even with a connection cost.
In addition to shedding light on why biological networks evolve to be hierarchical, this work also lends additional support to the hypothesis that a connection cost may also drive the emergence of hierarchy in humanconstructed networks, such as company organizations [42], road systems [54], and the Internet [10]. Furthermore, knowing how to evolve hierarchical networks can improve medical research [55, 56], which benefits from more biologically realistic, and thus hierarchical, network models [57, 58].
The ability to evolve hierarchy will also aid fields that harness evolution to automatically generate solutions to challenging engineering problems [59, 60], as it is known that hierarchy is a beneficial property in engineered designs [18, 61]. In fact, artificial intelligence researchers have long sought to evolve computational neural models that have the properties of modularity, regularity, and hierarchy [18, 62, 63, 64, 65], which are key enablers of intelligence in animal brains [18, 66, 67, 68]. It has recently been shown that combining techniques known to produce regularity [69] with a connection cost produces networks that are both modular and regular [36]. This work suggests that doing so can produce networks that have all three properties, a hypothesis we will confirm in future work. Being able to create networks with all three properties will both improve our efforts to study the evolution of natural intelligence and accelerate our ability to recreate it artificially.
Method and Materials
Experimental setup
There were 30 trials per treatment. Each trial is an independent evolutionary process that is initiated with a different random seed, meaning that the sequence of stochastic events that drive evolution (e.g. mutation, selection) are different. Each trial lasted 25000 generations and had a population size of 1000. Unless otherwise stated, all analyses and visualizations are based on the highestperforming network per trial (ties are broken randomly).
Statistics
The test of statistical significance is the MannWhitneyWilcoxon ranksum test, unless otherwise stated. We report and plot medians
95% bootstrapped confidence intervals of the medians, which are calculated by resampling the data 5000 times. For visual clarity we reduce the resampling noise inherent in bootstrapping by smoothing confidence intervals with a median filter (window size of 101).
Evolutionary algorithm
Networks evolve via a multiobjective evolutionary algorithm called the Nondominated Sorting Genetic Algorithm version II (NSGAII), which was first introduced in
[70]. While the original NSGAII weights all objectives equally, to explore the consequence of having the performance objective be more important than the connection cost objective, Clune et al. [17] created a stochastic version of NSGAII (called probabilistic NSGAII, or PNSGA) where each objective is considered for selection with a certain probability (a detailed explanation can be found in [17] and Fig. S15a). Specifically, performance factors into selection 100% of the time and the connection cost objective factors in percent of the time. Preliminary experiments for this paper demonstrated that values of of 0.25, 0.5, and 1.0 led to qualitatively similar results. However, for simplicity and because the largest differences between P&CC and PA treatments resulted from , we chose that value as the default for this paper. Note that when , NSGAII and PNSGA are identical.Behavioral diversity
Evolutionary algorithms notoriously get stuck in local optima (locally, but not globally, highfitness areas), in part because limited computational resources require smaller population sizes than are found in nature [60]. To make computational evolution more representative of natural evolutionary populations, which exhibit more diversity, we adopt the common technique of promoting behavioral diversity [60, 71, 72, 73, 74, 17] by adding another independent objective that rewards individuals for behaving differently than other individuals in the population. This diversity objective factored into selection 100% of the time. Preliminary experiments confirmed that this diversitypromoting technique is necessary. Without it, evolution does not reliably produce functional networks in either treatment, as has been previously shown when evolving networks with properties such as modularity [17, 36].
To calculate the behavioral diversity of a network, for each input we store a network’s output (response) in a binary vector; output values 0 are stored as 1 and 0 otherwise. How different a network’s behavior is from the population is calculated by computing the Hamming distance between that network’s output vector and all the output vectors of all other networks (and normalizing the result to get a behavioral diversity measure between and ).
Connection cost
Following [17], the connection cost of a network is computed after finding an optimal node placement for internal (hidden) nodes (input and output nodes have fixed locations) that minimizes the overall network connection cost. These locations can be computed exactly [75]. Optimizing the location of internal nodes is biologically motivated; there is evidence that the location of neurons in animal nervous systems are optimized to minimize the summed length of connections between them [37, 38, 75]. The overall network connection cost is then computed as the summed squared length of all connections. Network visualizations show these optimal neural placements.
Network nodes are located in a twodimensional Cartesian space (,). The locations of input and output nodes are fixed, and the locations of hidden nodes vary according to their optimal location as just described.
For all problems, the inputs have coordinates of , a coordinate of , and the output is located at (0,4), except for the problem in Fig. 8, right, which has an output located at because of the extra layer of hidden neurons.
Network model and its biological relevance
The network model is multilayered and feedforward, meaning a node at layer receives incoming connections only from nodes at layer and has outgoing connections only to nodes at layer . This network model is common for investigating questions in systems biology [50, 49, 76], including studies into the evolution of modularity [17, 77]. While the layered and feedforward nature of networks may contribute to elevated hierarchy, this network architecture is the same across all treatments and we are interested in the differences between levels of hierarchy that occur with and without a connection cost.
For the main problem, ANDXORAND, networks are of the form , which means that there are 8 input nodes, 3 hidden layers each having 4, 4 and 2 nodes respectively, and 1 output node. The integers {2, 1, 1, 2} are the possible values for connection weights, whereas the possible values for the biases are .
Information flows through the network in discrete time stpdf one layer at a time. The output of node is the result of the function:
(1) 
where is the set of nodes connected to node , is the connection strength between node and node , is the output value of node , and is a bias. The bias determines at which input value the output changes from negative to positive. The function,
, is an activation function that guarantees that the output of a node is in the range of
. The slope of the transition between the two extreme output values is determined by , which is here set to 20 (Figs. S15BC).Following (16), in all treatments, the initial number of connections in networks is randomly chosen between 20 and 100. If that chosen number of initial connections is more than the maximum number of connections that a network can have (which is 58), the network is fully connected. When random, valid networks are generated, the initial number of connections ranges from the minimum number needed, which is 11, to the maximum number possible, which is 58. Each connection is assigned a random weight selected from the possible values for connection weights and is placed between randomly chosen, unconnected neurons residing on two consecutive layers. Our results are qualitatively unchanged when the initial number of connections is smaller than the default, i.e. when evolution starts with sparse networks (Fig. S14).
Mutations
To create offspring, a parent network is copied and then randomly mutated. Each network has a chance of having a single connection added. Candidates for new connections are pairs of unconnected nodes that reside on two consecutive layers. Each network also has a chance of a randomly chosen connection being removed. Each node in the network has chance of its bias being incremented or decremented with both options equally probable; five values are available and mutations that produce values higher or lower than these values are ignored. Each connection in the network has chance of its weight being incremented or decremented, where is the total number of connections in the network. Because weights must be in the set of possible values , mutations that produce values higher or lower than these four values are ignored.
Modularity
Network modularity is calculated with an efficient approximation [78] of the commonly used Q metric developed by [79]. Because that metric has been extensively described before [79], here we only describe it briefly. The Q metric defines network modularity, for a particular division of the network, as the number of withinmodule edges minus the expected number of these edges in an equivalent network, where edges are placed randomly [79]. The formula for this metric is:
(2) 
Where and are the in and outdegree of node and , respectively, is the total number of edges in the network, is the connectivity matrix, which is 1 if there is an edge from node to , and 0 otherwise, and is a function whose value is 1 if nodes and belong to the same module, and otherwise.
Hierarchy
Our hierarchy measure comes from [12]. It ranks nodes based on their influence. A node’s influence on a network equals the portion of a network that is reachable from that node (respecting the fact that edges are directed). Based on this metric, the larger the proportion of a network a node can reach, via its outgoing edges, the more influential it is. For example, a root node has more influence because a path can be traced from it to every node in the network, whereas leaf nodes have no influence. The metric calculates network hierarchy by computing the heterogeneity of the influence values of all nodes in the network. Intuitively, nodeinfluence heterogeneity is high in hierarchical networks (where some nodes have a great deal of influence and others none), and low in nonhierarchical networks (e.g. in a fully connected network the influence of nodes is perfectly homogeneous).
Because of the nonlinear function that maps a node’s inputs to its output, even a small change in the input to a node can change whether it fires. For that reason, it is difficult to determine the influence one node has on another based on the strength of the connection between them. We thus calculate hierarchy scores by looking only at the presence of connections between nodes, ignoring the strength of those connections. The score for a weighted directed network is calculated by:
(3) 
is the highest influence value and represents a set of all nodes in the network. is the number of nodes in the network. Each node in the network is represented by . , the influence value of node , is given by:
(4) 
Here, is the length of the path that goes from node to node , meaning it is the number of outgoing connections along the path.
Functional hierarchy
As a proxy for quantifying functional hierarchy, we measure the percent of logic subproblems of an overall problem that are solved by part of a network. Note that an overall logic problem can be solved without solving any specific subproblem (e.g. in the extreme, if the entire problem is computed in one step). We determine whether a logic subproblem is solved as follows: for all possible inputs to a network, a neuron solves a logic gate (a subproblem) if, for its outputs, there exists any threshold value that correctly separates all the True answers from the False answers for the logic gate in question. We also consider a subproblem as solved if it is solved by a group of neurons on the same layer. To check for this case, we consider all possible groupings of neurons in a layer (groups of all sizes are checked). We sum the outputs of the neurons in a group and see if there is a threshold that correctly separates True and False for the logic subproblem on all possible network inputs. To prevent counting solutions multiple times, each subproblem is considered only once: i.e. the algorithm stops searching when a subproblem is found to be solved.
Supplementary Information
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