DOCSLIB.ORG

In Search of an Evolutionary Coding Style

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
In Search of an Evolutionary Coding Style View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Chalmers Research In search of an evolutionary coding style Downloaded from: https://research.chalmers.se, 2019-09-07 22:09 UTC Citation for the original published paper (version of record): Lundh, T. (2000) In search of an evolutionary coding style [Source Title missing], 00(03) N.B. When citing this work, cite the original published paper. research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library (article starts on next page) In search of an evolutionary coding style TorbjÄorn Lundh¤ Department of Mathematics S-412 96 GÄoteborg, Sweden [email protected] of the evolved program codes in his avida set up. He Keywords: style, code, evolution, arti¯cial life, emer- said something like this: gence, complexity, DNA, Avida, stylometry, GRASP, Halstead's complexity. \The codes that are evolved will eventually be almost totally unreadable. Things are never used only once, but two or more times. Abstract It is a kind of a `madman's' code." In the near future, all the human genes will be iden- In recent years, a lot of examples have been found ti¯ed. But understanding the functions coded in the where genes have been \reused" for di®erent purposes genes is a much harder problem. For example, by during development. As an example, take the runt using block entropy, one has that the DNA code is gene in Drosophila, which is used in sex determina- closer to a random code then written text, which in tion, segmentation and central nervous system cre- turn is less ordered then an ordinary computer code; ation. In [7] the authors write: see [17]. \As mature organisms we are composed of Instead of saying that the DNA is badly written, an astonishing array of diverse cell types| using our programming standards, we might say that all derived from a single-celled zygote. it is written in a di®erent style | an evolutionary When faced with the task of generating such style. cellular diversity in a reproducible fashion, We will suggest a way to search for such a style how has the embryo chosen to respond? Re- in a quanti¯ed manner by using an arti¯cial life pro- cent work in a number of developmental sys- gram, and by giving a de¯nition of general codes and tems has suggested that the embryo has em- a de¯nition of style for such codes. ployed two approaches. First, given ¯nite resources, the embryo has e±ciently chosen 1 Background to reutilize a limited set of proteins in di®er- ent temporal and spatial contexts to create Let us as a background cite three di®erent sources. cellular diversity. Second, the embryo has The ¯rst is J. Madox's comment on page 376 in also chosen to install molecular redundan- [11]. cies to ensure the reproducibility of these patterns from individual to individual." \In genetics for example, the task of under- standing the functions of all the 100,000 hu- How can we capture these comments about the man genes will require a much greater ef- style or quality of the computer code and the DNA, fort than that involved in their identi¯ca- in a quanti¯ed manner? Can we do that in such a tion, and by a factor 10 or more." general manner that we will be able to use analogous quality measure both for carbon{ and silicon based Chris Adami from Caltech made, in his survey talk genetic codes? at Renaissance Technologies in Stony Brook 10/27/98 about arti¯cial life, a brief remark about the quality 1.1 Plan of the paper ¤This work was done at SUNY Stony Brook under the sup- port (408003356-8) from the Swedish Natural Science Research We give a straightforward but general de¯nition of Council, NFR. a \code", a general de¯nition of the \style" of such Stony Brook IMS Preprint #2000/3 March 2000 2 TorbjornÄ Lundh codes using a given set of measures. We will also Finally I would like to thank D. Brander for play- suggest such measures of characteristic features, such ing squash with me and for trying to teach me some as some type of \madness", the robustness, and the grammar. (All the remaining language errors are nat- amount of reuse, of such codes. Finally, we will make urally completely my own fault.) some \baby{experiments" by running the avida pro- gram and analyze samples of the evolved code, gen- erate two simulated programs in the same function 2 A general code class, and eventually stylistically compare the real evolved code with these simulated codes, all of this We will give a general de¯nition of a code as a string will be done in a C++ program. Some results will be of generalized \letters" from a given alphabet, that graphically displayed at the end. when interpreted, will de¯ne a function. This inter- pretation is not unique, i.e. many di®erent codes will 1.2 Future goals produce the same function. That fact will give us a way to study classes of codes. That is, two codes are One could then more systematically run the avida in the same class if their interpretation gives equiva- program (or something similar) and analyze the lent functions. evolved codes stylistically with more realistic, and a higher number of comparasion codes. By changing 2.1 Codes parameters for the set up in avida, one might even- tually capture some common features. By comparing We will give a de¯nition of a code by using an under- the carbon based programming style and the silicon lying alphabet, and a generalized interpretor. based style, it might be possible to ¯nd some common parts that would describe the natural programming De¯nition 2.1 Let us de¯ne a code to be a ¯nite style for evolution. That would in turn help us read string of \letters" taken from an \alphabet", , A carbon based code. And it would also give us some k Code = ®i ; where ®i ; hints how to create more robust computer programs, f gi=1 2 A by looking at how nature has solved such problems. such that when the code is interpreted, the code will represent a well de¯ned function (or a process), 1.3 Questions Codej fj , with a domain Df such that for all in- ! j Is there an existing way to study the style or quality puts, x Dfj , to the interpretation of the code, will 2 1 of the DNA? And is there any existing theory in the give fj(x) as the output . information sciences that deals with this on the sili- con side? We are aiming at a complexity level higher 2.2 Classes of codes than the usual information theory measures, such as the notion of entropy (see the comment in Section From the above de¯nition of codes, we see that di®er- 6.2). ent codes can have the same function representation, see Example 2.5 below. 1.4 Acknowledgement Let us therefore introduce the following classi¯ca- tion. Let the code Codef and the codes Codefi have I would very much like to thank professor G. Thom- the functions f and fi as their representations. sen for listen to my ideas and for his constant encour- agement. I would also like to thank him for making De¯nition 2.2 Let the code class with respect to the me feel so much at home in his Xenopus lab, and function f be the following (in¯nite) set of codes. teaching me the basics in frog handling and cultural f = Codef : fi(x) = f(x); for all x Df : behaviour in the life sciences. C f i 2 g I would also like to thank Dr D. Slice for his interest and all his help and suggestions. Remark 2.3 If the interpretor is not able to produce B. Cohen has been a good source for me concerning a well de¯ned function from the code Code, we say that Code is in the error class ". That (huge) class the existing computer code complexity measures. He C has also been a fruitful discussion partner. can be viewed as the complement to all interpretable I am grateful to S. Sutherland for reading an earlier codes. version of this paper and giving me useful comments 1Note that we here consider a function in a general sense, and pointing out errors and weaknesses. i.e. not necessarily numerical. In Search of an Evolutionary Coding Style 3 Question 2.4 With the suggested norm of f below, of codes in f . If a given measure has a range outside C C is f compact? Do there exist extremal codes? [0; 1], let us use the transformation C x Example 2.5 Let x ! 1 + x 1 f(x) = ; x 2 to make it ¯t into [0; 1]. ¡ We de¯ne the following scalar measure. x+2 2 if x = 2 g(x) = x ¡4 6 ¡ ½ 1 if x = 2 ºw(Codeg) = w ¹(Codeg); ¡ 4 ¡ ¢ and where w is a normalized weight vector such that 1 4 if x = 2 w = 1, for some norm . For example, let ¡ 1 ¡ jj jj jj ¢ jj 8 2 if x = 0 > ¡ n 1 h(x) = > 1 if x = 1 p > ¡ p < 1 if x = 3 w = w p = wi ; jj jj jj jj µ j j ¶ 1 Xi=1 > 2 if x = 4 > :> Note that Codeg and Codef are both in h; and f = where p 1.
Load more

Recommended publications
  • Evolution of Digital Organisms in Truly Two-Dimensional Memory Space
    UKRAINIAN CATHOLIC UNIVERSITY BACHELOR THESIS Evolution of digital organisms in truly two-dimensional memory space Author: Supervisor: Mykhailo POLIAKOV Oleg FARENYUK A thesis submitted in fulfillment of the requirements for the degree of Bachelor of Science in the Department of Computer Sciences Faculty of Applied Sciences Lviv 2020 i Declaration of Authorship I, Mykhailo POLIAKOV, declare that this thesis titled, “Evolution of digital organisms in truly two-dimensional memory space” and the work presented in it are my own. I confirm that: • This work was done wholly or mainly while in candidature for a research de- gree at this University. • Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated. • Where I have consulted the published work of others, this is always clearly attributed. • Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. • I have acknowledged all main sources of help. • Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed my- self. Signed: Date: ii UKRAINIAN CATHOLIC UNIVERSITY Faculty of Applied Sciences Bachelor of Science Evolution of digital organisms in truly two-dimensional memory space by Mykhailo POLIAKOV Abstract Artificial life is the field of study where researchers use simulations to understand natural life. One of the notable simulations of artificial life is called Tierra.
    [Show full text]
  • Chapter 6.Qxp
    Testing Darwin Digital organisms that breed thousands of times faster than common bacteria are beginning to shed light on some of the biggest unanswered questions of evolution BY CARL ZIMMER F YOU WANT TO FIND ALIEN LIFE-FORMS, does this. Metabolism? Maybe not quite yet, but Ihold off on booking that trip to the moons of getting pretty close.” Saturn. You may only need to catch a plane to East Lansing, Michigan. One thing the digital organisms do particularly well is evolve. “Avida is not a simulation of evolu- The aliens of East Lansing are not made of car- tion; it is an instance of it,” Pennock says. “All the bon and water. They have no DNA. Billions of them core parts of the Darwinian process are there. These are quietly colonizing a cluster of 200 computers in things replicate, they mutate, they are competing the basement of the Plant and Soil Sciences building with one another. The very process of natural selec- at Michigan State University. To peer into their tion is happening there. If that’s central to the defi- world, however, you have to walk a few blocks west nition of life, then these things count.” on Wilson Road to the engineering department and visit the Digital Evolution Laboratory. Here you’ll It may seem strange to talk about a chunk of find a crew of computer scientists, biologists, and computer code in the same way you talk about a even a philosopher or two gazing at computer mon- cherry tree or a dolphin. But the more biologists itors, watching the evolution of bizarre new life- think about life, the more compelling the equation forms.
    [Show full text]
  • BEACON 2011 Annual Report
    BEACON Center for the Study of Evolution in Action ANNUAL REPORT November 1, 2011 For any questions regarding this report, please contact: Danielle J. Whittaker, Ph.D. Managing Director BEACON Center for the Study of Evolution in Action 1441 Biomedical and Physical Sciences Michigan State University East Lansing, MI 48824 517-884-2561 [email protected] BEACON 2011 Annual Report I. GENERAL INFORMATION Date submitted November 1, 2011 Reporting period February 1, 2011 – January 31, 2012 Name of the Center BEACON Center for the Study of Evolution in Action Name of the Center Director Erik D. Goodman Lead University Michigan State University Address 1441 Biomedical and Physical Sciences East Lansing, MI 48824 Phone Number 517-884-2555 Fax Number 517-353-7248 Center Director email [email protected] Center URL http://www.beacon-center.org Participating Institutions Institution 1 Name North Carolina A&T State University Contact Person Gerry Vernon Dozier Address Department of Computer Science 508 McNair Hall Greensboro, NC 27411 Phone Number (336) 334-7245, ext 467 Fax Number (336) 334-7244 Email Address [email protected] Role of Institution at Center Member Institution Institution 2 Name University of Idaho Contact Person James Foster Address Department of Biological Sciences Moscow, ID 83844-3051 Phone Number (208) 885-7062 Fax Number (208) 885-7905 Email Address [email protected] Role of Institution at Center Member Institution Institution 3 Name The University of Texas at Austin Contact Person Risto Miikkulainen Address Department of Computer Sciences 1 University Station D9500 Austin TX 78712-0233 Phone Number (512) 471-9571 Fax Number (512) 471-8885 Email Address [email protected] Role of Institution at Center Member Institution Institution 4 Name University of Washington Contact Person Benjamin Kerr Address Department of Biology Box 351800 Seattle, WA 98195 Phone Number (206)-221-3996 Fax Number Email Address [email protected] Role of Institution at Center Member Institution BEACON 2011 Annual Report I.
    [Show full text]
  • Editorial on Digital Organism
    Editorial Journal of Computer Science & Volume 13:6, 2020 DOI: 10.37421/jcsb.2020.13.325 Systems Biology ISSN: 0974-7230 Open Access Editorial on Digital Organism Chinthala Mounica* Department of Computer Science, Osmania University, India a growing number of evolutionary biologists. Evolutionary biologist Richard Editorial Note Lenski of Michigan State University has used Avida extensively in his work. Lenski, Adami, and their colleagues have published in journals such as Nature An advanced creature is a self-duplicating PC program that changes and and the Proceedings of the National Academy of Sciences (USA). Digital develops. Advanced creatures are utilized as an apparatus to contemplate the organisms can be traced back to the game Darwin, developed in 1961 at elements of Darwinian development, and to test or check explicit speculations Bell Labs, in which computer programs had to compete with each other by or numerical models of development. The investigation of computerized trying to stop others from executing. A similar implementation that followed creatures is firmly identified with the region of counterfeit life. this was the game Core War. In Core War, it turned out that one of the winning Digital organisms can be traced back to the game Darwin, developed in strategies was to replicate as fast as possible, which deprived the opponent of 1961 at Bell Labs, in which computer programs had to compete with each other all computational resources. Programs in the Core War game were also able to by trying to stop others from executing. A similar implementation that followed mutate themselves and each other by overwriting instructions in the simulated this was the game Core War.
    [Show full text]
  • Open-Ended Artificial Evolution
    February 6, 2008 18:23 WSPC/157-IJCIA alife-complexity-ijcia OPEN-ENDED ARTIFICIAL EVOLUTION RUSSELL K. STANDISH High Performance Computing Support Unit, University of New South Wales 2052, Australia [email protected], http://parallel.hpc.unsw.edu.au/rks Of all the issues discussed at Alife VII: Looking Forward, Looking Backward, the issue of whether it was possible to create an artificial life system that exhibits open-ended evolution of novelty is by far the biggest. Of the 14 open problems settled on as a result of debate at the conference, some 6 are directly, or indirectly related to this issue. Most people equate open-ended evolution with complexity growth, although a priori these seem to be different things. In this paper I report on experiments to measure the complexity of Tierran organisms, and show the results for a size-neutral run of Tierra. In this run, no increase in organismal complexity was observed, although organism size did increase through the run. This result is discussed, offering some signposts on path to solving the issue of open ended evolution. 1. Introduction An open discussion forum was conducted recently at Alife VII to draw up a list of open problems that might guide the field of artificial life in the coming years, styled on the famous Hilbert problems that guided much of Mathematics in the 20th century. The resulting list3 contains 14 open problems, divided into 3 categories: How does life arise from the non-living; What are the potentials and limits of living systems; How is life related to mind, machine and culture.
    [Show full text]
  • Arxiv:1803.03453V4 [Cs.NE] 21 Nov 2019
    The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman1†, Jeff Clune1, 2†, Dusan Misevic3†, Christoph Adami4, Lee Altenberg5, Julie Beaulieu6, Peter J Bentley7, Samuel Bernard8, Guillaume Beslon9, David M Bryson4, Patryk Chrabaszcz11, Nick Cheney2, Antoine Cully12, Stephane Doncieux13, Fred C Dyer4, Kai Olav Ellefsen14, Robert Feldt15, Stephan Fischer16, Stephanie Forrest17, Antoine Fr´enoy18, Christian Gagn´e6 Leni Le Goff13, Laura M Grabowski19, Babak Hodjat20, Frank Hutter11, Laurent Keller21, Carole Knibbe9, Peter Krcah22, Richard E Lenski4, Hod Lipson23, Robert MacCurdy24, Carlos Maestre13, Risto Miikkulainen26, Sara Mitri21, David E Moriarty27, Jean-Baptiste Mouret28, Anh Nguyen2, Charles Ofria4, Marc Parizeau 6, David Parsons9, Robert T Pennock4, William F Punch4, Thomas S Ray29, Marc Schoenauer30, Eric Schulte17, Karl Sims, Kenneth O Stanley1,31, Fran¸coisTaddei3, Danesh Tarapore32, Simon Thibault6, Westley Weimer33, Richard Watson34, Jason Yosinski1 †Organizing lead authors 1 Uber AI Labs, San Francisco, CA, USA 2 University of Wyoming, Laramie, WY, USA 3 Center for Research and Interdisciplinarity, Paris, France 4 Michigan State University, East Lansing, MI, USA 5 Univeristy of Hawai‘i at Manoa, HI, USA 6 Universit´eLaval, Quebec City, Quebec, Canada 7 University College London, London, UK 8 INRIA, Institut Camille Jordan, CNRS, UMR5208, 69622 Villeurbanne, France 9 Universit´ede Lyon, INRIA, CNRS, LIRIS UMR5205, INSA, UCBL,
    [Show full text]
  • Bibliographic and Annotated List of Peer-Reviewed Publications Supporting Intelligent Design
    BIBLIOGRAPHIC AND ANNOTATED LIST OF PEER-REVIEWED PUBLICATIONS SUPPORTING INTELLIGENT DESIGN UPDATED JULY, 2017 PART I: INTRODUCTION While intelligent design (ID) research is a new scientific field, recent years have been a period of encouraging growth, producing a strong record of peer-reviewed scientific publications. In 2011, the ID movement counted its 50th peer-reviewed scientific paper and new publications continue to appear. As of 2015, the peer-reviewed scientific publication count had reached 90. Many of these papers are recent, published since 2004, when Discovery Institute senior fellow Stephen Meyer published a groundbreaking paper advocating ID in the journal Proceedings of the Biological Society of Washington. There are multiple hubs of ID-related research. Biologic Institute, led by molecular biologist Doug Axe, is “developing and testing the scientific case for intelligent design in biology.” Biologic conducts laboratory and theoretical research on the origin and role of information in biology, the fine-tuning of the universe for life, and methods of detecting design in nature. Another ID research group is the Evolutionary Informatics Lab, founded by senior Discovery Institute fellow William Dembski along with Robert Marks, Distinguished Professor of Electrical and Computer Engineering at Baylor University. Their lab has attracted graduate-student researchers and published multiple peer-reviewed articles in technical science and engineering journals showing that computer programming “points to the need for an ultimate information source qua intelligent designer.” Other pro-ID scientists around the world are publishing peer-reviewed pro-ID scientific papers. These include biologist Ralph Seelke at the University of Wisconsin Superior, Wolf-Ekkehard Lönnig who recently retired from the Max Planck Institute for Plant Breeding Research in Germany, and Lehigh University biochemist Michael Behe.
    [Show full text]
  • Avida-ED Lab Book Summer 2017 Active LENS Workshop Version
    Avida-ED Lab Book Summer 2017 Active LENS Workshop Version Avidians in Action Avida-ED Project Curriculum Development Team Wendy Johnson, Cory Kohn, Amy Lark, Louise Mead, Robert T. Pennock, Jim Smith, Michael Wiser http://avida-ed.msu.edu http://bit.do/avida-ed This material is based in part upon work supported by the National Science Foundation under NSF-IUSE (NSF Grant # 1432563), "Active LENS: Learning Evolution and the Nature of Science using Evolution in Action", Robert T. Pennock, PI, Richard Lenski, Louise Mead, Charles Ofria, James J. Smith, co-PIs. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Preface This is the summer 2017 edition of the Avida-ED Lab Book as prepared for the Michigan State University course IBIO150 – Integrative Biology: From DNA to Populations – and the Avida-ED Active LENS Train-the Trainers Workshops. This edition’s activities have been substantially reworked for greater clarity and biological content depth, pedagogical scaffolding, and student engagement. Though the activities are similar to their past iterations, experienced Avida-ED instructors should refamiliarize themselves with the content. Of particular note, the Lab Book now contains a fourth focal Exercise, which investigates population change due to random processes – genetic drift. Based on feedback from Active LENS faculty development workshop participants, the 2016 edition reversed the order of exercises 2 and 3, and incorporated a change of terminology to help students better understand the factors that lead to changes in an organism’s fitness.
    [Show full text]
  • Interpreting the Tape of Life: Ancestry-Based Analyses Provide
    Interpreting the Tape of Life: Emily Dolson* Michigan State University Ancestry-Based Analyses Provide BEACON Center for the Study of Insights and Intuition about Evolution in Action Department of Computer Science Evolutionary Dynamics and Engineering Ecology, Evolutionary Biology, and Behavior Program [email protected] Alexander Lalejini Michigan State University Abstract Fine-scale evolutionary dynamics can be challenging to BEACON Center for the Study of tease out when focused on the broad brush strokes of whole Evolution in Action populations over long time spans. We propose a suite of diagnostic Department of Computer Science analysis techniques that operate on lineages and phylogenies in digital and Engineering evolution experiments, with the aim of improving our capacity to Ecology, Evolutionary Biology, quantitatively explore the nuances of evolutionary histories in and Behavior Program digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four Steven Jorgensen phylogeny measurements from biology: phylogenetic richness, Michigan State University phylogenetic divergence, phylogenetic regularity, and depth of the BEACON Center for the Study of most-recent common ancestor. In addition to quantitative metrics, Evolution in Action we also discuss several existing data visualizations that are useful for Department of Computer Science understanding lineages and phylogenies: state sequence visualizations, and Engineering fitness landscape overlays, phylogenetic trees, and Muller plots. We examine the behavior of these metrics (with the aid of data visualizations) in two well-studied computational contexts: (1) a set of Charles Ofria two-dimensional, real-valued optimization problems under a range of Michigan State University mutation rates and selection strengths, and (2) a set of qualitatively BEACON Center for the Study of different environments in the Avida digital evolution platform.
    [Show full text]
  • Avida-ED Lab Book Argentina 2019 Workshop Version
    Avida-ED Lab Book Argentina 2019 Workshop Version Avidian Tango? (freeimages.com) Avida-ED Project Curriculum Development Team Michigan State University NSF BEACON Center for the Study of Evolution in Action http://avida-ed.msu.edu ii This material is based in part upon work supported by the National Science Foundation under NSF-IUSE (NSF Grant # 1432563), "Active LENS: Learning Evolution and the Nature of Science using Evolution in Action", Robert T. Pennock, PI, Richard Lenski, Louise Mead, Charles Ofria, James J. Smith, co-PIs. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. iii iv Preface This version of the Avida-ED Lab Book was prepared for the Argentina 2019 Avida-ED Workshop at the Faculty of Exact and Natural Sciences of the University of Buenos Aires. The Lab Book represents the cumulative work many people, including the present Avida- ED Project Curriculum Development Team (Robert T. Pennock, Louise Mead, Jim Smith, and Mike Wiser), with significant contributions from former members Cory Kohn, Amy Lark, and Wendy Johnson. The Lab Book, as presented here, was assembled from component files by Jim Smith on May 9, 2019. Though the activities in this version of the Avida-ED Lab Book are similar to their past iterations, experienced Avida-ED instructors should re-familiarize themselves with the content. Of particular note, the Lab Book now contains a new Exercise #4, which investigates population change due to random processes – genetic drift. Based on feedback from Active LENS faculty development workshop participants, beginning with the 2016 edition we incorporated terminology changes to help students better understand the factors that lead to changes in an organism’s fitness.
    [Show full text]
  • Avida: a Software Platform for Research in Computational Evolutionary Biology
    Avida: A Software Platform for Research in Computational Evolutionary Biology Charles Ofria, David M. Bryson, and Claus O. Wilke Avida1 is a software platform for experiments with self-replicating and evolv- ing computer programs. It provides detailed control over experimental set- tings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. This chapter ex- plains the general principles on which Avida is built, its main components and their interactions, and gives an overview of some prior research with Avida. 1 Introduction to Avida When studying biological evolution, we have to overcome a large obstacle: Evolution is extremely slow. Traditionally, evolutionary biology has there- fore been a field dominated by observation and theory, even though some regard the domestication of plants and animals as early, unwitting evolution experiments. Realistically, we can carry out controlled evolution experiments only with organisms that have very short generation times, so that popula- tions can undergo hundreds of generations within a time frame of months or years. With the advances in microbiology, such experiments in evolution have become feasible with bacteria and viruses [16, 46]. However, even with mi- croorganisms, evolution experiments still take a lot of time to complete and are often cumbersome. In particular, some data can be difficult or impossible to obtain, and it is often impractical to carry out enough replicas for high statistical accuracy. According to Daniel Dennett, “...evolution will occur whenever and wher- ever three conditions are met: replication, variation (mutation), and differ- ential fitness (competition)” [11]. It seems to be an obvious idea to set up 1 Parts of the material in this chapter previously appeared in other forms [36, 35] 3 4 Ofria,Bryson,andWilke these conditions in a computer, and to study evolution in silico rather than in vitro.
    [Show full text]
  • Avida: a Software Platform for Research in Computational Evolutionary Biology
    Avida: A software platform for research in computational evolutionary biology Charles Ofria∗ Department of Computer Science and Engineering Michigan State University, E. Lansing, MI 48824 [email protected] Claus O. Wilke Digital Life Laboratory 136-93 California Institute of Technology, Pasadena, CA 91125 [email protected] December 3, 2003 ∗ Corresponding author, Phone: (517) 355-8389 1 Abstract Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over ex- perimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed. Keywords: digital organisms, experimental evolution, self-replicating com- puter programs 2 Contents 1 Introduction 5 1.1 History of digital life . 6 1.2 Overview of Avida . 9 2 Avida Organisms 10 2.1 Virtual hardware . 11 2.2 Genetic Language . 12 2.2.1 Template Matching and Heads . 13 2.2.2 Nop's as Modifiers . 15 2.2.3 Memory Allocation and Division . 16 2.3 Mutations . 18 2.4 Phenotype . 19 2.5 Genotype and Species . 20 3 Avida World 21 3.1 Scheduling . 22 3.2 World Topologies and Birth Methods . 23 3.3 Environment and Resources . 25 3.4 Organism Interactions . 26 4 Experimental Environment 28 4.1 The Genesis File . 28 4.2 The Instruction Set File . 31 4.3 The Ancestral Organism . 31 4.4 The Environment File .
    [Show full text]