Five years ago, Gregory Chaitin, a co-founder of the    fascinating and mind-bending field of algorithmic information    theory, offered a challenge:1  
      The honor of mathematics requires us to come up with a      mathematical theory of evolution and either prove that Darwin      was wrong or right!    
    In     Introduction to Evolutionary    Informatics2, co-authored by William    A. Dembski, Winston Ewert, and myself, we answer Chaitins    challenge in the negative: There exists no model successfully    describing undirected Darwinian evolution. Period. By model,    we mean definitive simulations or foundational mathematics    required of a hard science.  
    We show that no meaningful information can arise from an    evolutionary process unless that process is guided. Even when    guided, the degree of evolutions accomplishment is limited by    the expertise of the guiding information source  a limit we    call Baseners ceiling. An evolutionary program whose goal is    to master chess will never evolve further and offer investment    advice.  
    Here I answer ten frequently posed questions about and    objections to Introduction to Evolutionary Informatics.  
    1. Why yet another book dissing Darwinian evolution?  
    Solomon was right. Of    making many books there is no end, and much study wearies the    body.3 There are gobs of books written about    evolution, pro and con. Many are excellent. So whats so    important about Introduction to Evolutionary    Informatics? On the topic of evolution, the conclusion is    in: There exists no model successfully describing undirected    Darwinian evolution. Hard sciences are built on foundations of    mathematics or definitive simulations. Examples include    electromagnetics, Newtonian mechanics, geophysics, relativity,    thermodynamics, quantum mechanics, optics, and many areas in    biology. Those hoping to establish Darwinian evolution as a    hard science with a model have either failed or inadvertently    cheated. These models contain guidance mechanisms to land the    airplane squarely on the target runway despite stochastic wind    gusts. Not only can the guiding assistance be specifically    identified in each proposed evolution model, its contribution    to the success can be measured, in bits, as active    information.  
    And, as covered in Introduction to Evolutionary    Informatics, we suspect no model will ever exist to    substantiate the claims of undirected Darwinian evolution.  
    2. But Darwinian evolution is so complicated, it cant be    modeled!  
    If this objection is true, we have reached the same conclusion    by different paths: There exists no model successfully    describing undirected Darwinian evolution.  
    3. You model evolution as a search. Evolution isnt a    search.  
    We echo Billy Joel: We didnt start the fire! Models of    Darwinian evolution, Avida and EV included, are searches with a    fixed goal. For EV, the goal is finding specified nucleotide    binding sites. Avidas goal is to generate an EQU logic    function. Other evolution models that we examine in    Introduction to Evolutionary Informaticslikewise    seek a prespecified goal.  
    The evolution software Avida is of particular importance    because Robert Pennock, one of the co-authors of the first    paper describing Avida,4 gave testimony at the    Darwin-affirming Kitzmiller et al. v. Dover Area School    District bench trial. Pennocks testimony contributed to    Judge Joness ruling that teaching about intelligent design    violates the establishment clause of the United States    Constitution. Pennock testified, In the [Avida computer    program] system, were not simulating evolution. Evolution is    actually happening. If true, Avida and thus evolution are a    guided search with a specified target bubbling over with active    information supplied by the programmers.  
    The most celebrated attempt of an evolution model without a    goal of which were aware is TIERRA. In an attempt to recreate    something like the Cambrian explosion on a computer, the    programmer created what was thought to be an information-rich    environment where digital organisms would flourish and evolve.    According to TIERRAs ingenious creator, Thomas Ray, the    project failed and was abandoned. There has to date been no    success in open-ended evolution in the field of artificial    life.5  
    Therefore, there exists no model successfully describing    undirected Darwinian evolution.  
    4. You are not biologists. Why should anyone listen to you    about evolution?  
    Leave aside that this question reeks of the genetic fallacy    used in debate to steer conversation away from the topic at    hand and down a rabbit trail of credential defense. The    question is sincere, though, and deserves an answer. Besides,    it lets me talk about myself.  
    The truth is that computer scientists and engineers know a lot    about evolution and evolution models.  
    As we outline in Introduction to Evolutionary    Informatics, proponents of Darwinian evolution became giddy    about computers in the 1960s and 70s. Evolution was too slow to    demonstrate in a wet lab, but thousands and more generations of    evolution can be put in the bank when Darwinian evolution is    simulated on a computer. Computer scientists and engineers soon    realized that evolutionary search might assist in making    computer-aided designs. In Introduction to Evolutionary    Informatics, we describe how NASA engineers used guided    evolutionary programs to design antennas resembling bent paper    clips that today are floating and functioning in outer space.  
    Heres my personal background. I first became interested in    evolutionary computation late last century when I served as    editor-in-chief of the IEEE6 Transactions on    Neural Networks.7 I invited top researchers in    the field, David Fogel and his father Larry Fogel, to be the    guest editors of a special issue of my journal dedicated to    evolutionary computing.8 The issue was published in    January 1994 and led to David founding the IEEE Transactions    on Evolutionary Computing9 which today is the    top engineering/computer science journal dedicated to the    topic.  
    My first conference paper using evolutionary computing was    published a year later10 and my first journal    publication on evolutionary computation was in    1999.11 That was then. More recently my work, funded    by the Office of Naval Research, involves simulated evolution    of swarm dynamics motivated by the remarkable self-organizing    behavior of social insects. Some of the results were excitingly    unexpected12 including individual member suicidal    sacrifice to extend the overall lifetime of the    swarm.13 Evolving digital swarms is intriguing and    we have a whole web site devoted to the topic.14  
    So I have been playing in the evolutionary sandbox for a long    time and have dirt under my fingernails to prove it.  
    But is it biology? In reviewing our book for the American    Scientific Affiliation (ASA), my friend Randy Isaac, former    executive director of the ASA, said of our book, Those seeking    insight into biological or chemical evolution are advised to    look elsewhere.15 We agree! But if you are looking    for insights into the models and mathematics thus far proposed    by supporters of Darwinian evolution that purport to describe    the theory, Introduction to Evolutionary Informatics is    spot on. And we show there exists no model successfully    describing undirected Darwinian evolution.  
    5. You use probability inappropriately. Probability theory    cannot be applied to events that have already happened.  
    In the movie Dumb and Dumber, Jim Careys character,    Lloyd Christmas, is brushed off by beautiful Mary Samsonite    Swanson when told his chances with her areone in a    million. After a pause for introspective reflection, Lloyds    emergent toothy grin shows off his happy chipped tooth. He    enthusiastically blurts out, So youre telling me theres a    chance! Similar exclamationsare heard from Darwinian    evolutionist advocates. Darwinian evolution. So youre telling    me theres a chance! So again, we didnt start the probability    fire. Evolutionary models thrive on randomness described by    probabilities.  
    The probability-of-the -gaps championed by supporters of    Darwinian evolution is addressed in detail in Introduction    to Evolutionary Informatics. We show that the probability    resources of the universe and even string theorys hypothetical    multiverse are insufficient to explain the specified complexity    surrounding us.  
    Besides, a posteriori probability is used all the time.    The size of your last tweet can be measured in bits. Claude    Shannon, who coined the term bits in his classic 1948    paper,16 based the definition of the bit on    probability. Yet there sits your transmitted tweet with all of    its a posteriori bits fully exposed. Another example is    a posteriori Bayesian probability commonly used, for    example, in email spam filters. What is the probability that    your latest email from a Nigerian prince, already received and    written on your server, is spam? Bayesian probabilities are    also a posteriori probabilities.  
    So a hand-waving dismissal of a posteriori probabilities    is ill-tutored. The application of probability in    Introduction to Evolutionary Informatics is righteous    and the analysis leads to the conclusion that there exists no    model successfully describing undirected Darwinian evolution.  
    6. What about a biological anthropic principle? Were here,    so evolution must work.  
    Stephen Hawking has a simple explanation of the anthropic    principle: If the conditions in the universe were not suitable    for life, we would not be asking why they are as they are.    Gabor Csanyi, who quotes from Hawkings talk, says, Hawking    claims, the dimensionality of space and amount of matter in the    universe is [a fortuitous] accident, which needs no further    explanation.17  
    So youre telling me theres a chance!  
    The question ignored by anthropic principle enthusiasts is    whether or not an environment for even guided evolution could    occur by chance. If a successful search requires equaling or    exceeding some degree of active information, what is the chance    of finding any search with as good or better performance? We    call this a search-for-the-search. In Introduction to    Evolutionary Informatics, we show that the    search-for-the-search is exponentially more difficult that the    search itself! So if you kick the can down the road, the can    gets bigger.  
    Professor Sydney R. Coleman said after the Hawkings MIT talk,    Anything else is better [than the Anthropic Principle to    explain something].18 We agree. For example, check    out our search-for-the-search analysis in Introduction to    Evolutionary Informatics.  
    7. What about the claim that All information is    physical?  
    This is a question we have heard from physicists.  
    In physics, Landauers principle pertains to the lower    theoretical limit of energy consumption of computation and    leads to his statement all information is physical.  
    Saying All computers are mass and energy offers a similar    nearly useless description of computers. Like Landauers    principle, it suffers from the same overgeneralized vagueness    and is at best incomplete.  
    Claude Shannon counters Landauers claim:  
      It seems to me that we all define information as we choose;      and, depending upon what field we are working in, we will      choose different definitions. My own model of information      theorywas framed precisely to work with the problem of      communication.19    
    Landauer is probably correct within the narrow confines of his    physics foxhole. Outside the foxhole is Shannon information    which is built on unknown a priori probability of events    which have not yet happened and are therefore not yet physical.  
    We spend an entire chapter in Introduction to Evolutionary    Informatics defining information so there is no confusion    when the concept is applied. And we conclude there exists no    model successfully describing undirected Darwinian evolution.  
    8. Information theory cannot measure meaning.  
    Poppycock.  
    A hammer, like information theory, is a tool. A hammer can be    used to do more than pound nails. And information theory can do    more than assign a generic bit count to an object.  
    The most visible information theory models are Shannon    information theory and KCS information.20 The    consequence of Shannons theory on communication theory is    resident in your cell phone where codes predicted by Shannon    today allow maximally efficient use of available bandwidth. KCS    stands for Kolmogorov-Chaitin-Solomonoff information theory    named after the three men who independently founded the field.    KCS information theory deals with the information content of    structures. (Gregory Chaitin, by the way, gives a nice    nod-of-the-head to Introduction to Evolutionary    Informatics.21)  
    The manner in which information theory can be used to measure    meaning is addressed in Introduction to Evolutionary    Informatics. We explain, for example, why a picture of    Mount Rushmore containing imagesof fourUnited    States presidents has more meaning to you than a picture of    Mount Fuji even though both pictures might require the same    number of bits when stored on your hard drive. The degree of    meaning can be measured using a metric called algorithmic    specified complexity.  
    Rather than summarize algorithmic specified complexity derived    and applied in Introduction to Evolutionary Informatics,    we refer instead to a quote from a paper from one of the    worlds leading experts in algorithmic information theory, Paul    Vitnyi. The quote is from a paper he wrote over 15 years ago,    titled Meaningful Information.22  
      One can divide[KCS] information into two parts: the      information accounting for the useful regularity [meaningful      information] present in the object and the information      accounting for the remaining accidental [meaningless]      information.23    
    In Introduction to Evolutionary Informatics, we use    information theoryto measure meaningful information and    show there exists no model successfully describing undirected    Darwinian evolution.  
    9. To achieve specified complexity in nature, the fitness    landscape in evolution keeps changing. So, contrary to your    claim, Baseners ceiling doesnt apply in Darwinian    evolution.  
    In search, complexity cant be achieved beyond the expertise of    the guiding oracle. As noted, we refer to this limit as    Baseners ceiling.24However, if the fitness    continues to change, it is argued, the evolved entity can    achieve greater and greater specified complexity and ultimately    perform arbitrarily great acts like writing insightful    scholarly books disproving Darwinian evolution.  
    We analyze exactly this case in Introduction to Evolutionary    Informatics and dub the overall search structure stair    step active information. Not only is guidance required on    each stair, but the next step must be carefully chosen to guide    the process to the higher fitness landscape and therefore ever    increasing complexity. Most of the next possible choices are    deleterious and lead to search deterioration and even    extinction. This also applies in the limit when the stairs    become teeny and the stair case is better described as a ramp.    As Aristotle said, It is possible to fail in many wayswhile    to succeed is possible only in one way.  
    Heres an anecdotal illustration of the careful design needed    in the stair step model. If a meteor hits the Yucatan Peninsula    and wipes out all the dinosaurs and allows mammals to start    domination of the earth, then the meteors explosion must be a    Goldilocks event. If too strong all life on earth would be    zapped. If too weak, velociraptors would still be munching on    stegosaurus eggs.  
    Such fine tuning is the case of any fortuitous shift in fitness    landscapes and increases, not decreases, the difficulty of    evolution of ever-increasing specified complexity. It supports    the case there exists no model successfully describing    undirected Darwinian evolution.  
    10. Your research is guided by your ideology and cant be    trusted.  
    Theres that old derailing genetic fallacy again.  
    But yes! Of course, our research is impacted by our ideology!    We are proud to be counted among Christians such asthe    Reverend Thomas Bayes, Isaac Newton, George Washington Carver,    Michael Faraday, and the greatest of all mathematicians,    Leonard Euler.25 The truth of their contributions    stand apart from their ideology. But so does the work of    atheist Pierre-Simon Laplace. Truth trumps ideology. And    allowing the possibility of intelligent design, embraced by    enlightened theists and agnostics alike, broadens ones    investigative horizons.  
    Alan Turing, the brilliant father of computer science and    breaker of the Nazis enigma code, offers a great example of    the ultimate failure of ideology trumping truth. Asa    young man, Turing lost a close friend to bovine tuberculosis.    Devastated by the death, Turing turned from God and became an    atheist. He was partially motivated in his development of    computer science to prove man was a machine and consequently    that there was no need for a god. But Turings landmark work    has allowed researchers, most notably Roger    Penrose,26 to make the case that certain of mans    attributes including creativity and understanding are beyond    the capability of the computer. Turings ideological motivation    was thus ultimately trashed by truth.  
    The relationship between human and computer capabilities is    discussed in more depth in Introduction to Evolutionary    Informatics.  
    Take Aways  
    In Introduction to Evolutionary Informatics, Chaitins    challenge has been met in the negative and there exists no    model successfully describing undirected Darwinian evolution.    According to our current understanding, there never will be.    But science should never say never. As Stephen Hawking notes,    nothing in science is ever actually proved. We simply    accumulate evidence.27  
    So if anyone generates a model demonstrating Darwinian    evolution without guidance that ends in an object with    significant specified complexity, let us know. No guiding, hand    waving, extrapolation of adaptations, appealing to speculative    physics, or anecdotal proofs allowed.  
    Until then, I guess you can call us free-thinking skeptics.  
    Thanks for listening.  
    Robert J. Marks II PhD is Distinguished Professor of    Electrical and Computer Engineering at Baylor University.  
    Notes:  
    (1) Chaitin, Gregory. Proving Darwin: Making Biology    Mathematical. Vintage, 2012.  
    (2) Marks II, Robert J., William A. Dembski, and Winston Ewert.    Introduction to Evolutionary Informatics. World    Scientific, 2017.  
    (3) Ecclesiastes 12:12b.  
    (4) Lenski, R.E., Ofria, C., Pennock, R.T. and Adami, C., 2003.    The evolutionary origin of complex features. Nature,    423(6936), pp. 139-144.  
    (5) ID the Future podcast with Winston Ewert. Why    Digital Cambrian Explosions FizzleOr Fake It, June 7,    2017.  
    (6) IEEE, the Institute of Electrical and Electrical Engineers,    is the largest professional society in the world, with over    400,000 members.  
    (7) R.J. Marks II, The Joumal Citation Report: Testifying for    Neural Networks, IEEE Transactions on Neural Networks,    vol. 7, no. 4, July 1996, p. 801.  
    (8) Fogel, David B., and Lawrence J. Fogel. Guest editorial on    evolutionary computation, IEEE Transactions on Neural    Networks 5, no. 1 (1994): 1-14.  
    (9) R.J. Marks II, Old Neural Network Editors Dont Die, They    Just Prune Their Hidden Nodes, IEEE Transactions on Neural    Networks, vol. 8, no. 6 (November, 1997), p. 1221.  
    (10) Russell D. Reed and Robert J. Marks II, An Evolutionary    Algorithm for Function Inversion and Boundary Marking,    Proceedings of the IEEE International Conference on    Evolutionary Computation, pp. 794-797, November 26-30,    1995.  
    (11) C.A. Jensen, M.A. El-Sharkawi and R.J. Marks II, Power    Security Boundary Enhancement Using Evolutionary-Based Query    Learning, Engineering Intelligent Systems, vol. 7, no.    9, pp. 215-218 (December 1999).  
    (12) Jon Roach, Winston Ewert, Robert J. Marks II and Benjamin    B. Thompson, Unexpected Emergent Behaviors from Elementary    Swarms,Proceedings of the 2013 IEEE 45th Southeastern    Symposium on Systems Theory (SSST), Baylor University,    March 11, 2013, pp. 41-50.  
    (13) Winston Ewert, Robert J. Marks II, Benjamin B. Thompson,    Albert Yu, Evolutionary Inversion of Swarm Emergence Using    Disjunctive Combs Control, IEEE Transactions on Systems,    Man and Cybernetics: Systems, v. 43, #5, September 2013,    pp. 1063-1076.  
    Albert R. Yu, Benjamin B. Thompson, and Robert J. Marks II,    Swarm Behavioral Inversion for Undirected Underwater Search,    International Journal of Swarm Intelligence and Evolutionary    Computation, vol. 2 (2013). Albert R. Yu, Benjamin B.    Thompson, and Robert J. Marks II, Competitive Evolution of    Tactical Multiswarm Dynamics, IEEE Transactions on Systems,    Man and Cybernetics: Systems, vol. 43, no. 3, pp. 563- 569    (May 2013).  
    Winston Ewert, Robert J. Marks II, Benjamin B. Thompson, Albert    Yu, Evolutionary Inversion of Swarm Emergence Using    Disjunctive Combs Control, IEEE Transactions on Systems,    Man and Cybernetics: Systems, vol. 43, no. 5, September    2013, pp. 1063-1076.  
    (14) NeoSwarm.com.  
    (15) Review of Introduction to Evolutionary Informatics,    Perspectives on Science and Christian Faith, vol. 69 no.    2, June 2017, pp. 104-108.  
    (16) Claude E. Shannon, A mathematical theory of    communication, Bell System Technical Journal 27:    379-423 and 623656.  
    (17) Gabor Csanyi Stephen    Hawking Lectures on Controversial Theory, The Tech,    vol. 119, issue 48, Friday, October 8, 1999.  
    (18) The bracketed insertion in the quote is Csanyis, not    ours.  
    (19) Quoted in P. Mirowski, Machine Dreams: Economics    Becomes a Cyborg Science (New York: Cambridge University    Press, 2002), 170.  
    (20) Cover, Thomas M., and Joy A. Thomas. Elements of    Information Theory. John Wiley & Sons, 2012.  
    (21)     Review for Introduction to Evolutionary Informatics.  
    (22) Paul Vitnyi, Meaningful Information, in    International Symposium on Algorithms and Computation: 13th    International Symposium, ISAAC 2002, Vancouver, BC, Canada,    November 21-23, 2002.  
    (23) Unlike our approach, Vitnyis use of the so-called    Kolmogorov sufficient statistic here does not take context into    account.  
    (24) Basener, W.F., 2013. Limits of Chaos and Progress in    Evolutionary Dynamics. Biological Information  New    Perspectives. World Scientific, Singapore, pp. 87-104.  
    (25) Christian    Calculus.  
    (26) See, e.g., Penrose, Roger. Shadows of the Mind.    Oxford University Press, 1994.  
    (27) Hawking, Stephen. A Brief History of Time (1988).    AppLife, 2014.  
    Photo credit: Postman85, via Pixabay.  
Original post:
Top Ten Questions and Objections to Introduction to Evolutionary Informatics - Discovery Institute