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Stepney S, Hickinbotham S. On the Open-Endedness of Detecting Open-Endedness. ARTIFICIAL LIFE 2024; 30:390-416. [PMID: 36848499 DOI: 10.1162/artl_a_00399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
We argue that attempting to quantify open-endedness misses the point: The nature of open-endedness is such that an open-ended system will eventually move outside its current model of behavior, and hence outside any measure based on that model. This presents a challenge for analyzing Artificial Life systems, leading us to conclude that the focus should be on understanding the mechanisms underlying open-endedness, not simply on attempting to quantify it. To demonstrate this, we apply several measures to eight long experimental runs of the spatial version of the Stringmol automata chemistry. These experiments were originally designed to examine the hypothesis that spatial structure provides a defense against parasites. The runs successfully show this defense, but also show a range of innovative, and possibly open-ended, behaviors involved in countering a parasitic arms race. Commencing with system-generic measures, we develop and use a variety of measures dedicated to analyzing some of these innovations. We argue that a process of analysis, starting with system-generic measures but going on to system-specific measures, will be needed wherever the phenomenon of open-endedness is involved.
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2
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Channon A. A Procedure for Testing for Tokyo Type 1 Open-Ended Evolution. ARTIFICIAL LIFE 2024; 30:345-355. [PMID: 38635908 DOI: 10.1162/artl_a_00430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Tokyo Type 1 open-ended evolution (OEE) is a category of OEE that includes systems exhibiting the ongoing generation of adaptive novelty and ongoing growth in complexity. It can be considered as a necessary foundation for Tokyo Type 2 OEE (ongoing evolution of evolvability) and Tokyo Type 3 OEE (ongoing generation of major transitions). This article brings together five methods of analysis to form a procedure for testing for Tokyo Type 1 OEE. The procedure is presented as simply as possible, isolated from the complexities of any particular evolutionary system, and with a clear rationale for each step. In developing these steps, we also identify five key challenges in OEE. The last of these (achieving a higher order of complexity growth within a system exhibiting indefinitely scalable complexity) can be considered a grand challenge for Tokyo Type 1 OEE. Promising approaches to this grand challenge include also achieving one or both of Tokyo Types 2 and 3 OEE; this can be seen as one answer to why these other types of OEE are important, providing a unified view of OEE.
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3
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Schaible MJ, Szeinbaum N, Bozdag GO, Chou L, Grefenstette N, Colón-Santos S, Rodriguez LE, Styczinski MJ, Thweatt JL, Todd ZR, Vázquez-Salazar A, Adams A, Araújo MN, Altair T, Borges S, Burton D, Campillo-Balderas JA, Cangi EM, Caro T, Catalano E, Chen K, Conlin PL, Cooper ZS, Fisher TM, Fos SM, Garcia A, Glaser DM, Harman CE, Hermis NY, Hooks M, Johnson-Finn K, Lehmer O, Hernández-Morales R, Hughson KHG, Jácome R, Jia TZ, Marlow JJ, McKaig J, Mierzejewski V, Muñoz-Velasco I, Nural C, Oliver GC, Penev PI, Raj CG, Roche TP, Sabuda MC, Schaible GA, Sevgen S, Sinhadc P, Steller LH, Stelmach K, Tarnas J, Tavares F, Trubl G, Vidaurri M, Vincent L, Weber JM, Weng MM, Wilpiszeki RL, Young A. Chapter 1: The Astrobiology Primer 3.0. ASTROBIOLOGY 2024; 24:S4-S39. [PMID: 38498816 DOI: 10.1089/ast.2021.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
The Astrobiology Primer 3.0 (ABP3.0) is a concise introduction to the field of astrobiology for students and others who are new to the field of astrobiology. It provides an entry into the broader materials in this supplementary issue of Astrobiology and an overview of the investigations and driving hypotheses that make up this interdisciplinary field. The content of this chapter was adapted from the other 10 articles in this supplementary issue and thus represents the contribution of all the authors who worked on these introductory articles. The content of this chapter is not exhaustive and represents the topics that the authors found to be the most important and compelling in a dynamic and changing field.
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Affiliation(s)
- Micah J Schaible
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Nadia Szeinbaum
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - G Ozan Bozdag
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Luoth Chou
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
- Georgetown University, Washington DC, USA
| | - Natalie Grefenstette
- Santa Fe Institute, Santa Fe, New Mexico, USA
- Blue Marble Space Institute of Science, Seattle, Washington, USA
| | - Stephanie Colón-Santos
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
- Department of Botany, University of Wisconsin-Madison, Wisconsin, USA
| | - Laura E Rodriguez
- Lunar and Planetary Institute, Universities Space Research Association, Houston, Texas, USA
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - M J Styczinski
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- University of Washington, Seattle, Washington, USA
| | - Jennifer L Thweatt
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania, USA
| | - Zoe R Todd
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Alberto Vázquez-Salazar
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, California, USA
| | - Alyssa Adams
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
| | - M N Araújo
- Biochemistry Department, University of São Paulo, São Carlos, Brazil
| | - Thiago Altair
- Institute of Chemistry of São Carlos, Universidade de São Paulo, São Carlos, Brazil
- Department of Chemistry, College of the Atlantic, Bar Harbor, Maine, USA
| | | | - Dana Burton
- Department of Anthropology, George Washington University, Washington DC, USA
| | | | - Eryn M Cangi
- Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Tristan Caro
- Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA
| | - Enrico Catalano
- Sant'Anna School of Advanced Studies, The BioRobotics Institute, Pisa, Italy
| | - Kimberly Chen
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Peter L Conlin
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Z S Cooper
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Theresa M Fisher
- School of Earth and Space Exploration, Arizona State University, Tempe, Arizona, USA
| | - Santiago Mestre Fos
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Amanda Garcia
- Department of Bacteriology, University of Wisconsin-Madison, Wisconsin, USA
| | - D M Glaser
- Arizona State University, Tempe, Arizona, USA
| | - Chester E Harman
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ninos Y Hermis
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- Department of Physics and Space Sciences, University of Granada, Granada, Spain
| | - M Hooks
- NASA Johnson Space Center, Houston, Texas, USA
| | - K Johnson-Finn
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Owen Lehmer
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Ricardo Hernández-Morales
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Kynan H G Hughson
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Rodrigo Jácome
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Tony Z Jia
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
| | - Jeffrey J Marlow
- Department of Biology, Boston University, Boston, Massachusetts, USA
| | - Jordan McKaig
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Veronica Mierzejewski
- School of Earth and Space Exploration, Arizona State University, Tempe, Arizona, USA
| | - Israel Muñoz-Velasco
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ceren Nural
- Istanbul Technical University, Istanbul, Turkey
| | - Gina C Oliver
- Department of Geology, San Bernardino Valley College, San Bernardino, California, USA
| | - Petar I Penev
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Chinmayee Govinda Raj
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Tyler P Roche
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Mary C Sabuda
- Department of Earth and Environmental Sciences, University of Minnesota-Twin Cities, Minneapolis, Minnesota, USA
- Biotechnology Institute, University of Minnesota-Twin Cities, St. Paul, Minnesota, USA
| | - George A Schaible
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA
| | - Serhat Sevgen
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Institute of Marine Sciences, Middle East Technical University, Erdemli, Mersin, Turkey
| | - Pritvik Sinhadc
- BEYOND: Center For Fundamental Concepts in Science, Arizona State University, Arizona, USA
- Dubai College, Dubai, United Arab Emirates
| | - Luke H Steller
- Australian Centre for Astrobiology, and School of Biological, Earth and Environmental Sciences, University of New South Wales, Kensington, Australia
| | - Kamil Stelmach
- Department of Chemistry, University of Virginia, Charlottesville, Virginia, USA
| | - J Tarnas
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - Frank Tavares
- Space Enabled Research Group, MIT Media Lab, Cambridge, Massachusetts, USA
| | - Gareth Trubl
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
| | - Monica Vidaurri
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
- Department of Physics and Astronomy, Howard University, Washington DC, USA
| | - Lena Vincent
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
| | - Jessica M Weber
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | | | | | - Amber Young
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Northern Arizona University, Flagstaff, Arizona, USA
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4
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Colón-Santos S, Vázquez-Salazar A, Adams A, Campillo-Balderas JA, Hernández-Morales R, Jácome R, Muñoz-Velasco I, Rodriguez LE, Schaible MJ, Schaible GA, Szeinbaum N, Thweatt JL, Trubl G. Chapter 2: What Is Life? ASTROBIOLOGY 2024; 24:S40-S56. [PMID: 38498820 DOI: 10.1089/ast.2021.0116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
The question "What is life?" has existed since the beginning of recorded history. However, the scientific and philosophical contexts of this question have changed and been refined as advancements in technology have revealed both fine details and broad connections in the network of life on Earth. Understanding the framework of the question "What is life?" is central to formulating other questions such as "Where else could life be?" and "How do we search for life elsewhere?" While many of these questions are addressed throughout the Astrobiology Primer 3.0, this chapter gives historical context for defining life, highlights conceptual characteristics shared by all life on Earth as well as key features used to describe it, discusses why it matters for astrobiology, and explores both challenges and opportunities for finding an informative operational definition.
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Affiliation(s)
- Stephanie Colón-Santos
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
- Department of Botany, University of Wisconsin-Madison, Wisconsin, USA
| | - Alberto Vázquez-Salazar
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, California, USA
| | - Alyssa Adams
- Department of Botany, University of Wisconsin-Madison, Wisconsin, USA
| | | | - Ricardo Hernández-Morales
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Rodrigo Jácome
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Israel Muñoz-Velasco
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Laura E Rodriguez
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- Lunar and Planetary Institute, Universities Space Research Association, Houston, Texas, USA
| | - Micah J Schaible
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - George A Schaible
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Nadia Szeinbaum
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA
| | - Jennifer L Thweatt
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania, USA. (Former)
| | - Gareth Trubl
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
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5
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Stock M, Gorochowski TE. Open-endedness in synthetic biology: A route to continual innovation for biological design. SCIENCE ADVANCES 2024; 10:eadi3621. [PMID: 38241375 DOI: 10.1126/sciadv.adi3621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.
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Affiliation(s)
- Michiel Stock
- KERMIT & Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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6
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Stock M, Pieters O, De Swaef T, wyffels F. Plant science in the age of simulation intelligence. FRONTIERS IN PLANT SCIENCE 2024; 14:1299208. [PMID: 38293629 PMCID: PMC10824965 DOI: 10.3389/fpls.2023.1299208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/07/2023] [Indexed: 02/01/2024]
Abstract
Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.
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Affiliation(s)
- Michiel Stock
- KERMIT and Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Olivier Pieters
- IDLAB-AIRO, Ghent University, imec, Ghent, Belgium
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
| | - Tom De Swaef
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
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7
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Sienkiewicz R, Jędruch W. DigiHive: Artificial Chemistry Environment for Modeling of Self-Organization Phenomena. ARTIFICIAL LIFE 2023; 29:235-260. [PMID: 36787452 DOI: 10.1162/artl_a_00398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The article presents the DigiHive system, an artificial chemistry simulation environment, and the results of preliminary simulation experiments leading toward building a self-replicating system resembling a living cell. The two-dimensional environment is populated by particles that can bond together and form complexes of particles. Some complexes can recognize and change the structures of surrounding complexes, where the functions they perform are encoded in their structure in the form of Prolog-like language expressions. After introducing the DigiHive environment, we present the results of simulations of two fundamental parts of a self-replicating system, the work of a universal constructor and a copying machine, and the growth and division of a cell-like wall. At the end of the article, the limitations and arising difficulties of modeling in the DigiHive environment are presented, along with a discussion of possible future experiments and applications of this type of modeling.
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Affiliation(s)
- Rafał Sienkiewicz
- Gdańsk University of Technology, Faculty of Electronics, Telecommunications, and Informatics.
| | - Wojciech Jędruch
- Polish Naval Academy, Faculty of Mechanical and Electrical Engineering
- Gdańsk University of Technology, Faculty of Electronics, Telecommunications, and Informatics
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8
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Wong ML, Bartlett S, Chen S, Tierney L. Searching for Life, Mindful of Lyfe's Possibilities. Life (Basel) 2022; 12:783. [PMID: 35743813 PMCID: PMC9225093 DOI: 10.3390/life12060783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/03/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
We are embarking on a new age of astrobiology, one in which numerous interplanetary missions and telescopes will be designed, built, and launched with the explicit goal of finding evidence for life beyond Earth. Such a profound aim warrants caution and responsibility when interpreting and disseminating results. Scientists must take care not to overstate (or over-imply) confidence in life detection when evidence is lacking, or only incremental advances have been made. Recently, there has been a call for the community to create standards of evidence for the detection and reporting of biosignatures. In this perspective, we wish to highlight a critical but often understated element to the discussion of biosignatures: Life detection studies are deeply entwined with and rely upon our (often preconceived) notions of what life is, the origins of life, and habitability. Where biosignatures are concerned, these three highly related questions are frequently relegated to a low priority, assumed to be already solved or irrelevant to the question of life detection. Therefore, our aim is to bring to the fore how these other major astrobiological frontiers are central to searching for life elsewhere and encourage astrobiologists to embrace the reality that all of these science questions are interrelated and must be furthered together rather than separately. Finally, in an effort to be more inclusive of life as we do not know it, we propose tentative criteria for a more general and expansive characterization of habitability that we call genesity.
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Affiliation(s)
- Michael L. Wong
- Earth & Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA
| | - Stuart Bartlett
- Division of Geological & Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA; (S.B.); (S.C.)
| | - Sihe Chen
- Division of Geological & Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA; (S.B.); (S.C.)
| | - Louisa Tierney
- The Potomac School, Science Engineering & Research Center, McLean, VA 22101, USA;
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9
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Moreno MA, Ofria C. Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.750837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such transitions have profoundly shaped natural evolutionary history and occur in two forms: fraternal transitions involve lower-level entities that are kin (e.g., transitions to multicellularity or to eusocial colonies), while egalitarian transitions involve unrelated individuals (e.g., the origins of mitochondria). The necessary conditions and evolutionary mechanisms for these transitions to arise continue to be fruitful targets of scientific interest. Here, we examine a range of fraternal transitions in populations of open-ended self-replicating computer programs. These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enabled preferential communication and cooperation between cells. We repeatedly observed group-level traits that are characteristic of a fraternal transition. These included reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. We report eight case studies from replicates where transitions occurred and explore the diverse range of adaptive evolved multicellular strategies.
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10
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11
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Scott R, Gras R. A simulation study shows impacts of genetic diversity on establishment success of digital invaders in heterogeneous environments. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109173] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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12
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Abil Z, Danelon C. Roadmap to Building a Cell: An Evolutionary Approach. Front Bioeng Biotechnol 2020; 8:927. [PMID: 32974299 PMCID: PMC7466671 DOI: 10.3389/fbioe.2020.00927] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 07/20/2020] [Indexed: 12/20/2022] Open
Abstract
Laboratory synthesis of an elementary biological cell from isolated components may aid in understanding of the fundamental principles of life and will provide a platform for a range of bioengineering and medical applications. In essence, building a cell consists in the integration of cellular modules into system's level functionalities satisfying a definition of life. To achieve this goal, we propose in this perspective to undertake a semi-rational, system's level evolutionary approach. The strategy would require iterative cycles of genetic integration of functional modules, diversification of hereditary information, compartmentalized gene expression, selection/screening, and possibly, assistance from open-ended evolution. We explore the underlying challenges to each of these steps and discuss possible solutions toward the bottom-up construction of an artificial living cell.
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Affiliation(s)
| | - Christophe Danelon
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, Netherlands
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13
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Turney PD. Symbiosis Promotes Fitness Improvements in the Game of Life. ARTIFICIAL LIFE 2020; 26:338-365. [PMID: 32772859 DOI: 10.1162/artl_a_00326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We present a computational simulation of evolving entities that includes symbiosis with shifting levels of selection. Evolution by natural selection shifts from the level of the original entities to the level of the new symbiotic entity. In the simulation, the fitness of an entity is measured by a series of one-on-one competitions in the Immigration Game, a two-player variation of Conway's Game of Life. Mutation, reproduction, and symbiosis are implemented as operations that are external to the Immigration Game. Because these operations are external to the game, we can freely manipulate the operations and observe the effects of the manipulations. The simulation is composed of four layers, each layer building on the previous layer. The first layer implements a simple form of asexual reproduction, the second layer introduces a more sophisticated form of asexual reproduction, the third layer adds sexual reproduction, and the fourth layer adds symbiosis. The experiments show that a small amount of symbiosis, added to the other layers, significantly increases the fitness of the population. We suggest that the model may provide new insights into symbiosis in biological and cultural evolution.
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14
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Liard V, Parsons DP, Rouzaud-Cornabas J, Beslon G. The Complexity Ratchet: Stronger than Selection, Stronger than Evolvability, Weaker than Robustness. ARTIFICIAL LIFE 2020; 26:38-57. [PMID: 32027534 DOI: 10.1162/artl_a_00312] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Using the in silico experimental evolution platform Aevol, we have tested the existence of a complexity ratchet by evolving populations of digital organisms under environmental conditions in which simple organisms can very well thrive and reproduce. We observed that in most simulations, organisms become complex although such organisms are a lot less fit than simple ones and have no robustness or evolvability advantage. This excludes selection from the set of possible explanations for the evolution of complexity. However, complementary experiments showed that selection is nevertheless necessary for complexity to evolve, also excluding non-selective effects. Analyzing the long-term fate of complex organisms, we showed that complex organisms almost never switch back to simplicity despite the potential fitness benefit. On the contrary, they consistently accumulate complexity in the long term, meanwhile slowly increasing their fitness but never overtaking that of simple organisms. This suggests the existence of a complexity ratchet powered by negative epistasis: Mutations leading to simple solutions, which are favorable at the beginning of the simulation, become deleterious after other mutations-leading to complex solutions-have been fixed. This also suggests that this complexity ratchet cannot be beaten by selection, but that it can be overthrown by robustness because of the constraints it imposes on the coding capacity of the genome.
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Affiliation(s)
- Vincent Liard
- Inria Beagle Team
- Université de Lyon
- INSA-Lyon
- CNRS
- LIRIS
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15
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Inden B, Jost J. Evolving neural networks to follow trajectories of arbitrary complexity. Neural Netw 2019; 116:224-236. [DOI: 10.1016/j.neunet.2019.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/28/2018] [Accepted: 04/10/2019] [Indexed: 10/26/2022]
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16
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Abstract
Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely.
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Affiliation(s)
- Arend Hintze
- Michigan State University, Department of Integrative Biology, Department of Computer Science and Engineering, BEACON Center for the Study of Evolution in Action.
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17
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Sayama H. Cardinality Leap for Open-Ended Evolution: Theoretical Consideration and Demonstration by Hash Chemistry. ARTIFICIAL LIFE 2019; 25:104-116. [PMID: 31150289 DOI: 10.1162/artl_a_00283] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Open-ended evolution requires unbounded possibilities that evolving entities can explore. The cardinality of a set of those possibilities thus has a significant implication for the open-endedness of evolution. I propose that facilitating formation of higher-order entities is a generalizable, effective way to cause a cardinality leap in the set of possibilities that promotes open-endedness. I demonstrate this idea with a simple, proof-of-concept toy model called Hash Chemistry that uses a hash function as a fitness evaluator of evolving entities of any size or order. Simulation results showed that the cumulative number of unique replicating entities that appeared in evolution increased almost linearly along time without an apparent bound, demonstrating the effectiveness of the proposed cardinality leap. It was also observed that the number of individual entities involved in a single replication event gradually increased over time, indicating evolutionary appearance of higher-order entities. Moreover, these behaviors were not observed in control experiments in which fitness evaluators were replaced by random number generators. This strongly suggests that the dynamics observed in Hash Chemistry were indeed evolutionary behaviors driven by selection and adaptation taking place at multiple scales.
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Affiliation(s)
- Hiroki Sayama
- Binghamton University, Center for Collective Dynamics of Complex Systems.
- Waseda University, Waseda Innovation Lab
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18
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Packard N, Bedau MA, Channon A, Ikegami T, Rasmussen S, Stanley KO, Taylor T. An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue. ARTIFICIAL LIFE 2019; 25:93-103. [PMID: 31150285 DOI: 10.1162/artl_a_00291] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.
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19
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Abstract
The emergence of new replicating entities from the union of simpler entities characterizes some of the most profound events in natural evolutionary history. Such transitions in individuality are essential to the evolution of the most complex forms of life. Thus, understanding these transitions is critical to building artificial systems capable of open-ended evolution. Alas, these transitions are challenging to induce or detect, even with computational organisms. Here, we introduce the DISHTINY (Distributed Hierarchical Transitions in Individuality) platform, which provides simple cell-like organisms with the ability and incentive to unite into new individuals in a manner that can continue to scale to subsequent transitions. The system is designed to encourage these transitions so that they can be studied: Organisms that coordinate spatiotemporally can maximize the rate of resource harvest, which is closely linked to their reproductive ability. We demonstrate the hierarchical emergence of multiple levels of individuality among simple cell-like organisms that evolve parameters for manually designed strategies. During evolution, we observe reproductive division of labor and close cooperation among cells, including resource-sharing, aggregation of resource endowments for propagules, and emergence of an apoptosis response to somatic mutation. Many replicate populations evolved to direct their resources toward low-level groups (behaving like multicellular individuals), and many others evolved to direct their resources toward high-level groups (acting as larger-scale multicellular individuals).
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20
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Taylor T. Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems. ARTIFICIAL LIFE 2019; 25:207-224. [PMID: 31150286 DOI: 10.1162/artl_a_00290] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identified: exploratory, expansive, and transformational. These are characterized in terms of their relationship to the search space of phenotypic behaviors. A formalism is introduced to describe three key processes required for an evolutionary process: the generation of a phenotype from a genetic description, the evaluation of that phenotype, and the reproduction with variation of individuals according to their evaluation. The formalism makes explicit various influences in each of these processes that can easily be overlooked. The distinction is made between intrinsic and extrinsic implementations of these processes. A discussion then investigates how various interactions between these processes, and their modes of implementation, can lead to open-endedness. However, an important contribution of the article is the demonstration that these considerations relate to exploratory open-endedness only. Conditions for the implementation of the more interesting kinds of open-endedness-expansive and transformational-are also discussed, emphasizing factors such as multiple domains of behavior, transdomain bridges, and non-additive compositional systems. In contrast to a traditional Darwinian analysis, these factors relate not to the generic evolutionary properties of individuals and populations, but rather to the nature of the building blocks out of which individual organisms are constructed, and the laws and properties of the environment in which they exist. The article ends with suggestions of how the framework can be used to categorize and compare the open-ended evolutionary potential of different systems, how it might guide the design of systems with greater capacity for open-ended evolution, and how it might be further improved.
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Affiliation(s)
- Tim Taylor
- Monash University, Faculty of Information Technology.
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21
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Dolson EL, Vostinar AE, Wiser MJ, Ofria C. The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems. ARTIFICIAL LIFE 2019; 25:50-73. [PMID: 30933626 DOI: 10.1162/artl_a_00280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
- Emily L Dolson
- Michigan State University, BEACON Center for the Study of Evolution in Action, Department of Computer Science and Engineering Program in Ecology, Evolutionary Biology, and Behavior.
| | | | - Michael J Wiser
- Michigan State University, BEACON Center for the Study of Evolution in Action Program in Ecology, Evolutionary Biology, and Behavior.
| | - Charles Ofria
- Michigan State University, BEACON Center for the Study of Evolution in Action, Department of Computer Science and Engineering Program in Ecology, Evolutionary Biology, and Behavior.
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22
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Abstract
Rather than acting as a review or analysis of the field, this essay focuses squarely on the motivations for investigating open-endedness and the opportunities it opens up. It begins by contemplating the awesome accomplishments of evolution in nature and the profound implications if such a process could be ignited on a computer. Some of the milestones in our understanding so far are then discussed, finally closing by highlighting the grand challenge of formalizing open-endedness as a computational process that can be encoded as an algorithm. The main contribution is to articulate why open-endedness deserves a place alongside artificial intelligence as one of the great computational challenges, and opportunities, of our time.
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23
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Physical Universality, State-Dependent Dynamical Laws and Open-Ended Novelty. ENTROPY 2017. [DOI: 10.3390/e19090461] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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24
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Adamatzky A, Akl S, Burgin M, Calude CS, Costa JF, Dehshibi MM, Gunji YP, Konkoli Z, MacLennan B, Marchal B, Margenstern M, Martínez GJ, Mayne R, Morita K, Schumann A, Sergeyev YD, Sirakoulis GC, Stepney S, Svozil K, Zenil H. East-West paths to unconventional computing. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 131:469-493. [PMID: 28818636 DOI: 10.1016/j.pbiomolbio.2017.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 08/04/2017] [Accepted: 08/08/2017] [Indexed: 01/29/2023]
Abstract
Unconventional computing is about breaking boundaries in thinking, acting and computing. Typical topics of this non-typical field include, but are not limited to physics of computation, non-classical logics, new complexity measures, novel hardware, mechanical, chemical and quantum computing. Unconventional computing encourages a new style of thinking while practical applications are obtained from uncovering and exploiting principles and mechanisms of information processing in and functional properties of, physical, chemical and living systems; in particular, efficient algorithms are developed, (almost) optimal architectures are designed and working prototypes of future computing devices are manufactured. This article includes idiosyncratic accounts of 'unconventional computing' scientists reflecting on their personal experiences, what attracted them to the field, their inspirations and discoveries.
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Affiliation(s)
- Andrew Adamatzky
- Unconventional Computing Centre, University of the West of England, Bristol, UK; Unconventional Computing Ltd, Bristol, UK.
| | - Selim Akl
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Mark Burgin
- University of California at Los Angelos, USA
| | - Cristian S Calude
- Department of Computer Science, University of Auckland, Auckland, New Zealand
| | - José Félix Costa
- Departamento de Matemática, Instituto Superior Técnico, Centro de Filosofia das Ciências da Universidade de Lisboa, Portugal
| | | | | | - Zoran Konkoli
- Department of Microtechnology and Nanoscience - MC2, Chalmers University of Technology, Gothenburg, Sweden
| | - Bruce MacLennan
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA
| | | | - Maurice Margenstern
- Laboratoire d'Informatique Théorique et Appliquée, Université de Lorraine, Metz, France
| | - Genaro J Martínez
- Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico; Unconventional Computing Centre, University of the West of England, Bristol, UK
| | - Richard Mayne
- Unconventional Computing Centre, University of the West of England, Bristol, UK
| | | | - Andrew Schumann
- University of Information Technology and Management in Rzeszow, Rzeszow, Poland
| | - Yaroslav D Sergeyev
- University of Calabria, Rende, Italy and Lobachevsky State University, Nizhni Novgorod, Russia
| | - Georgios Ch Sirakoulis
- Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
| | - Susan Stepney
- Department of Computer Science, University of York, UK
| | - Karl Svozil
- Institute for Theoretical Physics, Vienna University of Technology, Austria
| | - Hector Zenil
- Algorithmic Dynamics Lab, Unit of Computational Medicine SciLifeLab and Center of Molecular Medicine, Karolinska Institute, Stockholm, Sweden
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25
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de Vladar HP, Santos M, Szathmáry E. Grand Views of Evolution. Trends Ecol Evol 2017; 32:324-334. [DOI: 10.1016/j.tree.2017.01.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 01/20/2017] [Accepted: 01/24/2017] [Indexed: 01/25/2023]
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26
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Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems. Sci Rep 2017; 7:997. [PMID: 28428620 PMCID: PMC5430523 DOI: 10.1038/s41598-017-00810-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 03/16/2017] [Indexed: 11/21/2022] Open
Abstract
Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) where the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.
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27
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Cartesian Genetic Programming in an Open-Ended Evolution Environment. PROGRESS IN ARTIFICIAL INTELLIGENCE 2017. [DOI: 10.1007/978-3-319-65340-2_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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28
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Taylor T, Bedau M, Channon A, Ackley D, Banzhaf W, Beslon G, Dolson E, Froese T, Hickinbotham S, Ikegami T, McMullin B, Packard N, Rasmussen S, Virgo N, Agmon E, Clark E, McGregor S, Ofria C, Ropella G, Spector L, Stanley KO, Stanton A, Timperley C, Vostinar A, Wiser M. Open-Ended Evolution: Perspectives from the OEE Workshop in York. ARTIFICIAL LIFE 2016; 22:408-423. [PMID: 27472417 DOI: 10.1162/artl_a_00210] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE-it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .
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