15,068 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
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Genetically Generated Neural Networks II: Searching for an Optimal Representation
Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval
Knowledge-based vision and simple visual machines
The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Selection, tinkering and emergence in complex networks: crossing the land of tinkering
Complex biological networks have very different origins than technologic ones. The latter involve extensive design and, as engineered structures, include a high level of optimization. The former involve (in principle) contingency and structural constraints, with new structures being incorporated through tinkering with previously evolved modules or units. However, the observation of the topological features of different biological nets suggests that nature can have a limited repertoire of ”attractors” that essentially optimize communication under some basic constraints of cost and architecture or that allow the biological nets to reach a high degree of homeostasis. Conversely, the topological features exhibited by some technology graphs indicate that tinkering and internal constraints play a key role, in spite of the ”designed” nature of these structures. Previous scenarios suggested to explain the overall trends of evolution are re-analyzed in light of topological patterns.Peer ReviewedPostprint (author's final draft
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