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Featured Is evolution a fact or theory?

Discussion in 'Creation & Theistic Evolution' started by mathinspiration, Jan 8, 2018.

  1. dcalling

    dcalling Senior Member

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    I have posted your previous post as well, and my question. It is very clear you used word "optimal" in all cases (and now you are trying to substitute with fitness peak, it won't work either because the engine example you used did reach optimal solution).
    Do you understand what an optimal solution is? If you do, why are you trying to substitute with fitness peak? and If you do understand what optimal solution is, how did the engineers know it is optimal if they don't know how the solution works?
     
  2. The Barbarian

    The Barbarian Crabby Old White Guy

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    No. "Optimal" is not a fitness peak. One of the reasons you're having trouble understanding this, is that you don't really get what genetic algorithms do.

    I don't think you understand what an optimal solution is, either. How much work have you done in general systems theory?

    It's not good enough to merely be familiar with coding.
     
  3. The Barbarian

    The Barbarian Crabby Old White Guy

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    Suppose you have a photograph you want to adjust to make it look better. You use three different processing applications, each using their autocorrect function to do so.

    One of them comes out better than the other two, with a sharper image and better color, which you wanted. How do you know that application worked best, if you don't know how the application works?

    If you get that, then you have your answer.
     
  4. dcalling

    dcalling Senior Member

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    This is based on individual taste, has nothing to do with our discussion.

    And, the end user might not understand how the application works, but the developers certainly does.
     
    Last edited: Nov 10, 2018
  5. dcalling

    dcalling Senior Member

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    You first tried to switch wording (optimal -> fitness peak). Now you are trying to make it sounds like I changed. Here is what you said and I said before. It is clear that you lack some basic understanding of computer science. Stop switching topics and answer my question, that might help you understand our discussion better.

    I have posted your previous post as well, and my question. It is very clear you used word "optimal" in all cases (and now you are trying to substitute with fitness peak, it won't work either because the engine example you used did reach optimal solution).
    Do you understand what an optimal solution is? If you do, why are you trying to substitute with fitness peak? and If you do understand what optimal solution is, how did the engineers know it is optimal if they don't know how the solution works?
     
  6. The Barbarian

    The Barbarian Crabby Old White Guy

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    Nope. I told you that they weren't the same thing. Why deny what's on the thread?

    You seem to have, but since you seem confused about the difference between a code and what the code produces, I think it's unfamiliarity with the issue.

    Nope. I'm trying to show you the difference between optima and fitness peaks.

    Barbarian asks, earlier:
    Because it's a better solution than anything that they could design. Do you understand what a "fitness peak" is?

    An optimal solution. There are local optima and global optima in systems analysis. Learn about it here:
    Local vs. Global Optima - MATLAB & Simulink

    See above. After you read it, you will understand how they differ from fitness peaks.

    This paper is a fairly accessible description of the way fitness peaks relate to local and global optima:
    Proc Biol Sci. 2016 Aug 17; 283(1836): 20160984.
    Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations

    Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it.
    ...
    The maximum fitness peak of this local landscape corresponds to the genotype that carries non-synonymous mutation P3/C3140U and synonymous mutation NIa-Pro/C6906U (table 1). Hereafter and for the sake of abbreviating the terminology, genotypes are going to be represented as binary strings in which a 0 will represent the wild-type alleles and a 1 the mutant allele. By using this terminology, the ancestral tobacco-adapted genotype can be represented as 00000 while the TEV-At17 genotype carrying all five mutations by 11111. The global optimum can then be written as 01001.


    Because knowing the software does not equate to knowing what the solution is.

    You probably would find a little reading in general systems analysis and population genetics to be helpful.
    The goal of systems theory is systematically discovering a system's dynamics, constraints, conditions and elucidating principles (purpose, measure, methods, tools, etc.) that can be discerned and applied to systems at every level of nesting, and in every field for achieving optimized equifinality. General systems theory is about broadly applicable concepts and principles, as opposed to concepts and principles applicable to one domain of knowledge.

    Knowing coding is not a substitute for knowing how systems work.
     
  7. dcalling

    dcalling Senior Member

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    because you are saying one thing and when you talk you obviously contradict what you have said. I have attached your statements regarding it below, just read your own statements.

    If they don't understand how the solution works, just because they have not reached a better solution yet, does not make the best solution optimal, it only means that is the best solution they found so far, but it maybe not be optimal, not even local optimal. do you understand this?

    This is the part you truely lost me. You are trying to apply a general principle over to something that does not apply, i.e. "Knowing coding is not a substitute for knowing how systems work". While your second statement is true (knowing some localized coding is not a substitute), it has almost nothing to do with our current discussion. You are just trying to apply your copy paste skills to something you does not understand at all and pretend you know about it.

    Below are all your previous statements, enjoy.

    I have posted your previous post as well, and my question. It is very clear you used word "optimal" in all cases (and now you are trying to substitute with fitness peak, it won't work either because the engine example you used did reach optimal solution).
    Do you understand what an optimal solution is? If you do, why are you trying to substitute with fitness peak? and If you do understand what optimal solution is, how did the engineers know it is optimal if they don't know how the solution works?
     
  8. The Barbarian

    The Barbarian Crabby Old White Guy

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    I contradicted what you claimed I said. As you now realize, I did not say optima were fitness peaks. I'm not sure why you continue to insist that I did, after I showed you otherwise several times.

    As you learned, the reason genetic algorithms produce better results than design, is that they copy evolutionary processes. As you also learned, they work, even when the engineers don't understand why they are better.

    You seem to know something about coding, but you seem to have never studied general systems theory at all.
     
  9. dcalling

    dcalling Senior Member

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    Below are all your previous statements, here is how it goes. You said "they don't always know how the optimal solutions found by their code works", I asked "how did the engineers know it is optimal if they don't know how the solution works?"

    And you first asked 'Do you understand what a "fitness peak" is', then after I pressed on you, asked "How much work have you done in general systems theory?", which is totally laughable in our context.

    Let's reset and ask the question again and see if you can give a straight answer. How did the engineers know it is optimal if they don't know how the solution works? In the case of the car engine algorithm, was that a local optimal solution or global optimal solution? And in the case of the antena algorithm, was that a local/global or not optimal solution?

    My quote:
     
  10. The Barbarian

    The Barbarian Crabby Old White Guy

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    Because the simulation went through a huge number of possible solutions, and selected the best one. How do engineers know when a designed solution is optimal? It works better than anything else that has been designed.

    If genetic algorithms didn't find solutions that are better than anything designed, engineers wouldn't use them. But they do find better solutions. And that being so, they use those solutions, even if they don't understand how the solution is better than others. It turns out that random mutation and natural selection are more efficient than design. Why wouldn't they be?

    It's not all that different from what people did in the past. They'd change something a little and see how it worked. If it worked better, they used it. Even if they didn't know that the atomic structure of atoms in optical glass determined refractive index, they tended to use glass that had those properties. They didn't know how it worked, they just knew certain materials tended to give them a better result. Eventually, someone realized that there was a rough correlation between density and refraction. They still didn't know why, but they used it.

    Coding is just the map. It's not the territory.
     
  11. dcalling

    dcalling Senior Member

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    As I said before, just because a solution is better than all current ones does not make it optimal. optimal solution is either max/min of what we seek, and we only know if it is optimal if we know the solution.

    So in the case of antennae design, the solution is not optimal, it is only one of the best they can get given current computation power, or they might not yet have a model to find optimal so they can only approx it.

    Now given that information, let's ask one more question, was your engine design example optimal or not?

    Once we arrive the answer for that question, we can go dig deeper into what we really want to discuss.

    As a reference, some of your important claims are listed below.
    My quote:
     
  12. The Barbarian

    The Barbarian Crabby Old White Guy

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    In fact, for very complex problems, there's no mathematical or feasible computational way to prove a solution is globally optimal. The reason genetic algorithms are used is that they find better solutions than can be done by design. Why do you suppose God used random mutation and natural selection?

    That's why I asked what you knew about fitness peaks. Genetic algorithms can find local optima, but there is always the possibility that there is a global optimum not found. Again, the reason that engineers use genetic algorithms is that they produce better solutions than design.

    Let's see what the engineers say...
    Optimization of Diesel Engine Emissions and Fuel Efficiency Using Genetic Algorithms and Phenomenological Model with EGR, Injection Timing and Multiple …
    https://www.researchgate.net/public..._Model_with_EGR_Injection_Timing_and_Multiple

    They seem to think so...

    Optimization of automotive diesel engine calibration using genetic algorithm techniques
    Energy

    Volume 158, 1 September 2018, Pages 807-819

    So do they.

    Optimization of a HSDI Diesel Engine for Passenger Cars Using a Multi-Objective Genetic Algorithm and Multi-Dimensional Modeling
    Hai-Wen Ge, Yu Shi, Rolf D. Reitz, David D. Wickman and Werner Willems


    SAE International Journal of Engines

    Vol. 2, No. 1 (2009), pp. 691-713

    They do, too. Guess so.

    I really do think that this would be easier for you, if you spent a little time learning about general systems.
     
  13. dcalling

    dcalling Senior Member

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    Your first part of statement is fit for us, but not for God, since you can't imagine the power of God, and God knows the solution for very complex problems. Where we have to proximate, God knows how to get global optimal directly.

    Now slow down and think about it, how do you know it is even local optimal?

    Now that is the answer I am looking for. for your diesel engine example, the solution is optimal. How do they know the solution is optimal without knowing the solution? Do you see your problem now?

    As a reference, some of your important claims are listed below.
    My quote:
     
  14. The Barbarian

    The Barbarian Crabby Old White Guy

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    Barbarian observes:
    In fact, for very complex problems, there's no mathematical or feasible computational way to prove a solution is globally optimal. The reason genetic algorithms are used is that they find better solutions than can be done by design. Why do you suppose God used random mutation and natural selection?

    There's the issue of elegance in nature. Everything, when we finally understand it, turns out to be simple and efficient. Hence mutation and natural selection. Maxium efficiency, minimum of rules.

    I've spent a lifetime thinking about it. Perhaps you should be open to the way He does it.

    Because it works better than anything else, after many, many iterations.

    At least locally.

    It works better. After many, many iterations, and all sorts of variations, the algorithms settles on the best result. Do you see your problem now?

    (demial that evolutionary processes can produce optimum results)

    Barbarian suggests:
    Let's see what the engineers say...
    Optimization of Diesel Engine Emissions and Fuel Efficiency Using Genetic Algorithms and Phenomenological Model with EGR, Injection Timing and Multiple …
    https://www.researchgate.net/public..._Model_with_EGR_Injection_Timing_and_Multiple

    They seem to think so...

    Optimization of automotive diesel engine calibration using genetic algorithm techniques
    Energy
    Volume 158, 1 September 2018, Pages 807-819


    So do they.

    Optimization of a HSDI Diesel Engine for Passenger Cars Using a Multi-Objective Genetic Algorithm and Multi-Dimensional Modeling
    Hai-Wen Ge, Yu Shi, Rolf D. Reitz, David D. Wickman and Werner Willems

    SAE International Journal of Engines

    Vol. 2, No. 1 (2009), pp. 691-713


    They do, too. Guess so.

    I really do think that this would be easier for you, if you spent a little time learning about general systems.
     
  15. dcalling

    dcalling Senior Member

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    Now first, we don't know for sure God used random mutation or designed first and let them mutate. My guess is God designed first, and allowed mutation to happen.

    Both of your answers are wrong, as for higher order function f(x), plot many times does not guaranty your solution is even locally optimal. And if you read the link you posted, the engine example archived Pareto optimum. Which means it is not just some answer that is better than some limited number of iterations, even if it is unlimited tries it is still the best solution. For complex problems we might not be able to figure out the f(x) for it and have to settle with approximation, but God most likely can.


    As a reference, some of your important claims are listed below.
    My quote:
     
  16. The Barbarian

    The Barbarian Crabby Old White Guy

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    It comes down to evidence. We have no evidence of design whatsoever. But we have a massive amount of evidence for random mutation and natrual selection.

    Hypotheses for science must be testable. Your guess is not. It's a perfectly valid religion, but it can't be science.

    As you learned, they are entirely correct. You're just having trouble figuring out how it works.

    You're describing evolution, if there was no such thing as natural selection. This is why engineers use genetic algorithsm; they give better results than design. God knew best, you see.

    I get that this is difficult for you. But the bottom line is simple; random mutation and natural selection works better than design for complex problems. This is why engineers use them.

    When you understand how that works, your questions are all answered.
     
    Last edited: Nov 15, 2018
  17. seltie

    seltie New Member

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    I think that we are all a sign that evolution exists, but how it will evolve - no one can assume. It's interesting to read on this topic the book of Yuval Noah Harari 'Sapiens', on which I wrote an essay for answershark.com site, where he gives examples of the development of the future and the evolution of human. Of course, this is largely a conjecture of the author, but some moments in our life have already been embodied, and human development takes place in parallel with the development of science.
     
  18. The Barbarian

    The Barbarian Crabby Old White Guy

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    What's scary is that we are approaching the point where we could direct our own evolution. There are some deep and troubling ethical questions associated with that.

    Genetic alteration of human embryos could eliminate tragedies like muscular dystrophy, but they could also be used to "improve" perfectly normal people. And of course the person so altered would have no voice in his or her "improvement."

    Bioethicists are now debating and trying to work out moral guidelines in this matter.
     
  19. dcalling

    dcalling Senior Member

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    How are you repeatably test/verify you can evolve some primate to human? Did you also put massive amount of assumption as well?

    Above is my question, below is your answer:
    Conclusion:
    As you have learned, they said their solution is Pareto optimum, they know their solution. So you are totally wrong on this one. Not sure why it is so difficult for you to understand this.
     
  20. dcalling

    dcalling Senior Member

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    This is where our perspective on evolution really matters :)
    So from my perspective, the ethics issue is not where we can improve normal people (as we our design will always be inferior to God's), it is more about when we mess up, how do we deal with the defective products (in this case they are actual people).
     
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