57 research outputs found
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
The evolutionary origins of hierarchy
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.Comment: 32 page
Guiding Neuroevolution with Structural Objectives
The structure and performance of neural networks are intimately connected,
and by use of evolutionary algorithms, neural network structures optimally
adapted to a given task can be explored. Guiding such neuroevolution with
additional objectives related to network structure has been shown to improve
performance in some cases, especially when modular neural networks are
beneficial. However, apart from objectives aiming to make networks more
modular, such structural objectives have not been widely explored. We propose
two new structural objectives and test their ability to guide evolving neural
networks on two problems which can benefit from decomposition into subtasks.
The first structural objective guides evolution to align neural networks with a
user-recommended decomposition pattern. Intuitively, this should be a powerful
guiding target for problems where human users can easily identify a structure.
The second structural objective guides evolution towards a population with a
high diversity in decomposition patterns. This results in exploration of many
different ways to decompose a problem, allowing evolution to find good
decompositions faster. Tests on our target problems reveal that both methods
perform well on a problem with a very clear and decomposable structure.
However, on a problem where the optimal decomposition is less obvious, the
structural diversity objective is found to outcompete other structural
objectives -- and this technique can even increase performance on problems
without any decomposable structure at all
Does Aligning Phenotypic and Genotypic Modularity Improve the Evolution of Neural Networks?
International audienceMany argue that to evolve artificial intelligence that rivals that of natural animals, we need to evolve neural networks that are structurally organized in that they exhibit modularity, regularity, and hierarchy. It was recently shown that a cost for network connections, which encourages the evolution of modularity, can be combined with an indirect encoding , which encourages the evolution of regularity, to evolve networks that are both modular and regular. However, the bias towards regularity from indirect encodings may prevent evolution from independently optimizing di↵erent modules to perform different functions, unless modularity in the phenotype is aligned with modularity in the genotype. We test this hypothesis on two multi-modal problems—a pattern recognition task and a robotics task—that each require di↵erent phenotypic modules. In general, we find that performance is improved only when genotypic and phenotypic modularity are encouraged simultaneously, though the role of alignment remains unclear. In addition, intuitive manual decompositions fail to provide the performance benefits of automatic methods on the more challenging robotics problem , emphasizing the importance of automatic, rather than manual, decomposition methods. These results suggest encouraging modularity in both the genotype and phenotype as an important step towards solving large-scale multi-modal problems, but also indicate that more research is required before we can evolve structurally organized networks to solve tasks that require multiple, different neural modules
Variation in reversal learning by three generalist mesocarnivores
Urbanization imposes novel challenges for wildlife, but also provides new opportunities for exploitation. Generalist species are commonly found in urban habitats, but the cognitive mechanisms facilitating their successful behavioral adaptations and exploitations are largely under-investigated. Cognitive flexibility is thought to enable generalists to be more plastic in their behavior, thereby increasing their adaptability to a variety of environments, including urban habitats. Yet direct measures of cognitive flexibility across urban wildlife are lacking. We used a classic reversal-learning paradigm to investigate the cognitive flexibility of three generalist mesocarnivores commonly found in urban habitats: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), and coyotes (Canis latrans). We developed an automated device and testing protocol that allowed us to administer tests of reversal learning in captivity without extensive training or experimenter involvement. Although most subjects were able to rapidly form and reverse learned associations, we found moderate variation in performance and behavior during trials. Most notably, we observed heightened neophobia and a lack of habituation expressed by coyotes. We discuss the implications of such differences among generalists with regard to urban adaptation and we identify goals for future research. This study is an important step in investigating the relationships between cognition, generalism, and urban adaptation
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