Hmm isn't it supposed to be small successive steps? Generation 5, who's your daddy?
Posted on 05/08/2009 4:25:57 PM PDT by GodGunsGuts
Using “Evolutionary Algorithms” by Intelligent Design
May 8, 2009 — Evolution can’t be all bad if scientists can use it to optimize your car. Science Daily said that scientists in Germany are “simulating evolution” to come up with ways to optimize difficult problems. Using “Evolutionary Algorithms”, they can discover solutions for engineering problems like water resource management and the design of brakes, airbags and air conditioning systems in automobiles. The simulated evolution program searches through a large number of random possibilities to make numerous successive slight improvements.
“The algorithms are called ‘evolutionary’ because the characteristics of evolution – mutation, recombination and selection – form the basis of their search for promising solutions,” the article claimed. Solutions that show promise are mutated and further selected.
Conferences on Evolutionary Algorithms are held each year and the interest in them is spreading into other disciplines. “The Evolutionary Algorithms are therefore a collective term for the various branches of research which have gradually developed: evolution strategies, evolutionary programming, genetic algorithms and genetic programming.”
Every once in awhile we need to give a refresher course about these reports, to show why the terminology is ludicrous. This has nothing to do with evolution and everything to do with intelligent design. Calling these“evolutionary algorithms” is like calling Eugenie Scott a creationist. Evolutionary Algorithm is an oxymoron – if it is evolutionary, it is not an algorithm, and if it is an algorithm, it is not evolutionary. Why? Because the essence of evolution, as Charles Darwin conceived it, has nothing to do with intelligent selection. Evolution is mindless, purposeless, and without a goal. These scientists, by contrast, have clear goals in mind. They are consciously and purposefully selecting the products of randomness to get better designs – intelligent designs. They may not know what the computer program will produce, but they sure well programmed the computer, and put in the criteria for success. Employing randomness in a program does nothing to make it evolutionary. The hallmark of intelligence is having a desired end and pulling it out of the soup of randomness. This is something evolution cannot do – unless one is a pantheist or animist, attributing the properties of a Universal Soul to nature. Undoubtedly, the NCSE would decry that. They can barely tolerate theistic evolutionists – the well-meaning but misguided Christians who try to put God in the role of the engineer who uses evolutionary algorithms for his purposes (e.g., man).
Remember – if it has purpose in it, it is not evolution. We must avoid equivocation. To discuss evolution with clarity it is essential to understand the terms and not mix metaphors. Charlie lept from artificial selection (intelligent design) to natural selection (materialism) only as a pedagogical aid. He did not intend for natural selection to have a mind like the goal-directed farmer or breeder uses. To think evolution, think mindless. Notice that itself is a one-way algorithm. You can think mindless, but the mindless cannot think.
For a definitive, in-depth treatment on why evolutionary algorithms cannot be mixed with evolution, see the book No Free Lunch in the Resource of the Week entry above.
Absolutely. I am one such person, as are the science and biology professors at most Christian universities.
Gen. 5:4 says Adam had other children, sons and daughters, after Seth. How could you miss that?
Hmm isn't it supposed to be small successive steps? Generation 5, who's your daddy?
Then what an apostle of Christ wrote at Romans 5:12 fits evolutionary theory?
==OK, where in Genesis does it say 6 consecutive days?
After each day, there was evening, and then there was morning, which began the next day. C-Y-C pretty much covered your Cain question. But beyond that, Jesus said He created us male and female right from the beginning, and the Bible says death did not enter the world until Adam and Eve sinned (both of which you failed to address). I would say each one of these rules out evolution, but when taken together, God’s Word most definitely renders evolution impossible.
LOL!
PS You never did give me your thoughts on the following re: GA’s...
http://creation.com/genetic-algorithms-are-irrelevant-to-evolution
To follow up on what GGG said, a further reason for opposition to evolution is that if we deny what Jesus said when referring back to Adam, we are implying that He is a liar. If He were to be a liar, He could not be our Savior.
Fools
[[The simulated evolution program]]
There is NO such program- There are however programs that prove intelligent design is needed.
“However, GAs do not mimic or simulate biological evolution because with a GA:
—A ‘trait’ can only be quantitative so that any move towards the objective can be selected for. Many biological traits are qualitative—it either works or it does not, so there is no step-wise means of getting from no function to the function.
—A single trait is selected for, whereas any living thing is multidimensional. A GA will not work with three or four different objectives, or I dare say even just two. A GA does not test for survival; it tests for only a single trait. Even with the simplest bacteria, which are not at all simple, hundreds of traits have to be present for it to be viable (survive); selection has to operate on all traits that affect survival.
—Something always survives to carry on the process. There is no rule in evolution that says that some organism(s) in the evolving population will remain viable no matter what mutations occur. In fact, the GAs that I have looked at artificially preserve the best of the previous generation and protect it from mutations or recombination in case nothing better is produced in the next iteration. This has a ratchet effect that ensures that the GA will generate the desired outcome—any move in the right direction is protected. This is certainly the case with Dawkins’ (in)famous ‘Weasel’ simulation—see Weasel Words and Dawkins’ weasel revisited.
—Perfect selection (selection coefficient, s = 1.0) is often applied so that in each generation only the best survives to ‘reproduce’ to produce the next generation. In the real world, selection coefficients of 0.01 or less are considered realistic, in which case it would take many generations for an information-adding mutation to permeate through a population. Putting it another way, the cost of substitution is ignored (see ReMine’s The Biotic Message for a thorough run-down of this, which is completely ignored in GAs—see Population genetics, Haldane’s Dilemma, etc.).
—The flip side to this is that high rates of ‘reproduction’ are used. Bacteria can only double their numbers per generation. Many ‘higher’ organisms can only do a little better, but GAs commonly produce 100s or 1000s of ‘offspring’ per generation. For example, if a population of 1,000 bacteria had only one survivor (999 died), then it would take 10 generations to get back to 1,000.
—Generation time is ignored. A generation can happen in a computer in microseconds whereas even the best bacteria take about 20 minutes. Multicellular organisms have far longer generation times.
—The mutation rate is artificially high (by many orders of magnitude). This is sustainable because the ‘genome’ is small (see next point) and artificial rules are invoked to protect the best ‘organism’ from mutations, for example. Such mutation rates in real organisms would result in all the offspring being non-viable (error catastrophe). This is why living things have exquisitely designed editing machinery to minimize copying errors to the rate of one in about 10 billion (for humans).
—The ‘genome’ is artificially small and only does one thing. The smallest real world genome is over 0.5 million base pairs (and it is an obligate parasite, which depends on its host for many of the substrates needed) with several hundred proteins coded. This is equivalent to over a million bits of information. Even if a GA generated 1800 bits of real information, as one of the commonly-touted ones claims, that is equivalent to maybe one small enzyme—and that was achieved with totally artificial mutation rates, generation times, selection coefficients, etc., etc. In fact, this is also how the body’s immune system develops specific antibodies, with these designed conditions totally different to any whole organism. This is pointed out in more detail by biophysicist Dr. Lee Spetner in his refutation of a skeptic.
—In real organisms, mutations occur throughout the genome, not just in a gene or section that specifies a given trait. This means that all the deleterious changes to other traits have to be eliminated along with selecting for the rare desirable changes in the trait being selected for. This is ignored in GAs.
—There is no problem of irreducible complexity with GAs (see Behe’s Darwin’s Black Box). Many biological traits require many different components to be present, functioning together, for the trait to exist at all (e.g. protein synthesis, DNA replication, reproduction of a cell, blood clotting, every metabolic pathway, etc.).
—Polygeny (where a trait is determined by the combined action of more than one gene) and pleiotropy (where one gene can affect several different traits) are ignored. Furthermore, recessive genes are ignored (recessive genes cannot be selected for unless present as a pair; i.e. homozygous), which multiplies the number of generations needed to get a new trait established in a population. The problem of recessive genes leads to one facet of Haldane’s Dilemma, where the well-known evolutionist J.B.S. Haldane pointed out that, based on the theorems of population genetics, there has not been enough time for the sexual organisms with low reproductive rates and long generation times to evolve. See review of ReMine’s analysis of Haldane’s Dilemma.
—Multiple coding genes are ignored. From the human genome project, it appears that, on average, each gene codes for at least three different proteins (see Genome Mania — Deciphering the human genome. In microbes, genes have been discovered that code for one protein when ‘read’ in one direction and a different protein when read backwards, or when the ‘reading’ starts one letter on. Creating a GA to generate such information-dense coding would seem to be out of the question. Such demands an intelligence vastly superior to human beings for its creation.
—The outcome in a GA is ‘pre-ordained’. Evolution is by definition purposeless, so no computer program that has a pre-determined goal can simulate it—period. This is blatantly true of Dawkins’ ‘weasel’ program, where the selection of each letter sequence is determined entirely on its match with the pre-programmed goal sequence. Perhaps if the programmer could come up with a program that allowed anything to happen and then measured the survivability of the ‘organisms’, it might be getting closer to what evolution is supposed to do! Of course that is impossible (as is evolution).
—With a particular GA, we need to ask how much of the ‘information’ generated by the program is actually specified in the program, rather than being generated de novo. A number of modules or subroutines are normally specified in the program, and the ways these can interact is also specified. The GA program finds the best combinations of modules and the best ways of interacting them. The amount of new information generated is usually quite trivial, even with all the artificial constraints designed to make the GA work.
For the above reasons (and some of them overlap), and no doubt there are more that could be added, GAs do not validate evolution. It does not take long with a decent calculator to see that the information space available for a minimal real world organism of just several hundred proteins is so huge that no naturalistic iterative real world process could have accounted for it—or even the development of a new protein with a new trait.
Another type of ‘simulation’ is that of antitheist T.D. Schneider.[1] Schneider claims that his program simulates the naturalistic formation of DNA binding sites for gene control. This exercise has led to grandstanding by some evolutionists that this proves creationists wrong. However, many of the same problems outlined above also apply to this programming exercise. For example, the selection coefficient is extremely high, the genome is extremely small, the mutation rate high, no possibility of extinction is permitted, etc. For many other problems, see the critique by Dr Royal Truman.
http://www.trueorigin.org/geneticalgorithms1.asp
He isn’t goign to give you his thoughts on that link GGG because computer algorithms DO NOT mimic nature in any way shape or form- they are intelligently designed, carefully controlled environments(despite their claism of ‘randomness’), carefully controlled processes that are simply NOT found in nature
Be sure to give this link a read too as it points out the ludicrous idea that computer models simulate some supposed imaginary evolutionary process:
http://www.trueorigin.org/schneider.asp
"Religion... is a smile on a dog."
Unlike the fictitious positive and negative integers used in the simulation, in earlier papers the weight matrix was derived using real data on functional sites(10) [143]. Known binding sites were selected from genbank, lined up and the proportion of each of the 4 bases found at each position of a sequence was determined (see Appendix).
Binding of a protein to DNA or RNA is rarely the simple matter implied by the computer program, but generally requires cooperation with other carefully crafted proteins(11). For example, transcription in eukaroytes is regulated by a group of gene-specific activator and repressor proteins[24] at specific binding sites. Simulating the production of one recognizer member of such ensembles by random point mutations has not been justified nor validated as being biologically conceivable. Instead, an arbitrary proportion of positive and negative integers in the computer program defined how to converge towards a short term goal flawlessly irrespective of any biological selective significance or stochastic effects.
How is chance to know a random mutation would lead towards developing a binding interaction? ‘Rsequence does not tell us anything about the physical mechanism a recognizer uses to contact the nucleic acid.’ [25]
Lacking any intelligence to choose, 3 dimensional shapes on the regulatory protein must be generated to permit the exact binding with a specific DNA sequence, like a well-meshed machine. That is why a methionine-carrying tNRA is able to identify a very short sequence on mRNA, AUG, and position a physically large m-RNA properly at the ribosome complex: it is due to the specialized geometry prepared at the ribosome’s P site. There is nothing biologically remarkable about AUG alone. Crystallographic, molecular modelling and cryo-electron microscopy studies have shed insight as how such feats are possible. Translating an mRNA strand one codon at a time requires the whole ribosome complex to act in a synchronized fashion, aptly described as a rachet-like mechanism[26]. The cell’s survival depends on ribosomes being able to locate the binding sites correctly [27](12).
Exactly how polypeptides are supposed to be able to identify that a location is or will become a useful binding site is deemed irrelevant: ‘As mentioned above, the exact form of the recognition mechanism is immaterial because of the generality of information theory.’ [1] Quite the contrary, for a realistic evolutionary simulation such physical details are critically relevant, and is a fatal oversight in the simulation. It is assumed random point mutations provide half the 64 member population with a 100% effective survival advantage, based on fine tuning of a single type of binding site under development. This is geometrically and thermodynamically unrealistic. Developing such precise binding interactions, one random mutation at a time, has nothing to do with the mathematics of information theory and needs to be quantitatively simulated based on physical realities. Any assumption of recognizable Darwinian selectivity for the intermediate stages needs to be quantitatively justified.
The requirements on recognizer and binding site are generally very stringent a must be close to perfect to be of any use whatsoever [All Emphasis' added by me]
The whole article is very itneresting and exposes just how NON RANDOM these supposed 'random evolutionary models' really are- it takes a great deal of intelligent design infact, and unrealistic biological scenarios, to 'make htese systems work' as 'nature intended' naturalism to function lol
careful, God does not take mocking lightly- just ask those famous people who mocked Him publicly only to die untimely deaths-
Thanks for the ping!
What about intellectual honesty on the evo side? Where is the evidence that systems within organisms evolved, let alone the organisms themselves? Before you cast aspersions upon anyone’s integrity, at least explain how say, a kidney or liver evolved, step by step. I’ll be checing back. I don’t believe in evolution b/c it defies common sense and scientific examination.
http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/V/VertebrateKidneys.html
Please explain in detail step by step how evolution defies scientific examination
Yes I did. I said the author doesn't understand GAs. And it's painfully obvious to anyone who's actually programmed them that he is ignorant of how they work. There is no way to "address" his statements since he doesn't even define what a GA is properly; it would make as much sense as me demanding you to address how the spark plugs work in your bicycle.
Chronologically this is after Cain slew Abel and bore Enoch. Where did Cain's wife came from? There is NO mention in the Bible of other children anywhere before Genesis 4:17. In fact, the Bible is VERY explicit in that Adam and Eve do NOT have another child until Genesis 4:25, after Enoch is born.
How can you miss that? Unless you're saying the timeline of the Bible is not exact?
Then what an apostle of Christ wrote at Romans 5:12 fits evolutionary theory?
Yes, yes it does. Evolution is a scientific theory of how man evolved from primates; Romans 5:12 is theology which tells us that once man became aware of God, that first man sinned and caused separation for all men from God.
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