scholarly journals The MODES toolbox: Measurements of Open-ended Dynamics in Evolving Systems

2018 ◽  
Author(s):  
Emily L Dolson ◽  
Anya E Vostinar ◽  
Michael J Wiser ◽  
Charles A Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
Digital Evolution ◽  
Evolutionary Systems ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

scholarly journals The MODES toolbox: Measurements of Open-ended Dynamics in Evolving Systems

2018 ◽  
Author(s):  
Emily L Dolson ◽  
Anya E Vostinar ◽  
Michael J Wiser ◽  
Charles A Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
Digital Evolution ◽  
Evolutionary Systems ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

scholarly journals The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems

2019 ◽  
Vol 25 (1) ◽  
pp. 50-73 ◽  
Author(s):  
Emily L. Dolson ◽  
Anya E. Vostinar ◽  
Michael J. Wiser ◽  
Charles Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
Digital Evolution ◽  
Evolutionary Systems ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK landscapes and the Avida digital evolution platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

scholarly journals The MODES toolbox: Measurements of Open-ended Dynamics in Evolving Systems

2018 ◽  
Author(s):  
Emily L Dolson ◽  
Anya E Vostinar ◽  
Michael J Wiser ◽  
Charles A Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
System Change ◽  
Digital Evolution ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure commonly-agreed-upon hallmarks of open-ended evolution in a system: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide a C++ implementation of these metrics that should be easy to connect to existing artificial life systems. As the field reaches consensus about additional hallmarks of open-ended evolution, metrics corresponding to these additions can be added to this toolbox. For example, we hope to soon add a measurement of the potential for major transitions in individuality to occur. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

scholarly journals The MODES toolbox: Measurements of Open-ended Dynamics in Evolving Systems

2018 ◽  
Author(s):  
Emily L Dolson ◽  
Anya E Vostinar ◽  
Michael J Wiser ◽  
Charles A Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
System Change ◽  
Digital Evolution ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure commonly-agreed-upon hallmarks of open-ended evolution in a system: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide a C++ implementation of these metrics that should be easy to connect to existing artificial life systems. As the field reaches consensus about additional hallmarks of open-ended evolution, metrics corresponding to these additions can be added to this toolbox. For example, we hope to soon add a measurement of the potential for major transitions in individuality to occur. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

scholarly journals The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

2020 ◽  
Vol 26 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Joel Lehman ◽  
Jeff Clune ◽  
Dusan Misevic ◽  
Christoph Adami ◽  
Lee Altenberg ◽  
...  
Keyword(s):  
Evolutionary Computation ◽  
Artificial Life ◽  
Oral Tradition ◽  
Natural World ◽  
Digital Evolution ◽  
Evolving Systems ◽  
Digital Organisms ◽  
Single Data Point ◽  
Single Data ◽  
Algorithmic Process

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Resilient Individuals Improve Evolutionary Search

2006 ◽  
Vol 12 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Terence Soule
Keyword(s):  
Evolutionary Computation ◽  
Artificial Life ◽  
Fitness Landscape ◽  
Evolutionary Search ◽  
Negative Effects ◽  
Evolutionary Dynamic ◽  
Genetic Robustness ◽  
Over Time ◽  
Evolving Populations

Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individual to resist the potentially negative effects of genetic operations. This article demonstrates a previously unobserved evolutionary dynamic: in populations initially favoring a low, broad fitness peak, increases in resiliency result in the population shifting to a higher, narrower fitness peak. In these cases increasing resiliency is a necessary precondition for finding narrower peaks.

scholarly journals Tracking the Trajectories of Evolution

2004 ◽  
Vol 10 (4) ◽  
pp. 397-411 ◽  
Author(s):  
Mikhail S. Burtsev
Keyword(s):  
Dynamical System ◽  
Bifurcation Diagram ◽  
Artificial Life ◽  
Evolutionary Systems

This article proposes a method of visualizing and measuring evolution in artificial life simulations. The evolving population of agents is treated as a dynamical system. The proposed method is inspired by the notion of trajectory. The article provides examples of tracking of trajectories of evolutionary systems in the spaces of genotypes, strategies, and some global characteristics. Visualization similar to a bifurcation diagram is used to represent results of a series of simulations.

Sign in / Sign up

Export Citation Format

Share Document