Now, apply all that to an evolutionary optimization program operating on a chemical information processing system [within life -- as explained and published by Hubert Yockey] searching a space within a quantum computer system [of our universe -- as hypothesized by Seth Lloyd].
Here [link: http://www.aics-research.com/research/notes.html] also, are some published notes [from IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 5, NO. 1, JANUARY, 1994. pp. 130-148.] explaining how computational simulations of evolution are basically representations of biological evolution. In fact, the author states that “Genetic algorithms are basically a proper simulation of Darwinian evolution. A population of trials is mutated and the best N are retained at each generation,” and, “Darwinian evolution, as a process, is an optimization algorithm. It is not a predictive theory, nor is it a tautology ([5] p. 519, [6] p. 112), as has often been claimed (e.g., [7],[8]). As in most optimization processes, the point(s) of solution wait to be discovered by trial-and-error search.”
(As an aside, which is interesting but not necessary to our discussion:
When discussing philosophical issues, the author states: “ Most troubling has been the elucidation of purpose. In distinct contrast to engineering, where purpose within a design is taken for granted -- and where the author of a design may perhaps still be available for questioning as to his reasons and motivations for specific details -- no such recourse is possible in naturally evolved systems. Indeed, the degree to which to even recognize the nature and extent of purpose within naturally evolved biota has proven to be one of biology's longest and most fundamental internal debates. Haldane once quipped, "Teleology [the study of purpose] is like a mistress to a biologist; he cannot live without her but he's unwilling to be seen with her in public" ([50] p. 392).
But purpose clearly exists in the designs produced by evolution and the reintroduction of purpose into the biological discussion has been championed by biologists such as Pittendridge, Lorenz and Mayr. Pittendridge [52] renamed and redefined the study of purpose in evolved structures to be teleonomy in order to draw as sharp a distinction between it and the mysticism of an older teleology as currently now exists between astronomy and astrology ([49] p. 29).”
... and ...
“When the philosophical perspective is constrained to the clear chain of causation resident in P, "...the designs developed by evolution are so similar in principal to those that would be reached by a conscious designer, ...it seems reasonable to suggest as a general approach to biological problems that the investigator should ask himself what are the essential functions involved and how might a designer provide for them" ([57] p. 4).)
Now, with the understanding that computational evolutionary simulations are understood to be “proper simulations of Darwinian evolution” which operates off an actual information processing system within a larger quantum computer (as hypothesized), please provide any evidence that this biological evolution is a blind process with no problem specific information and no knowledge of optimization problem matched to search algorithm beforehand. In fact, according to what the authors of the NFL Theorems state above, you won’t be able to guarantee anything of the sort using any computational simulation (which then poses the problem as to why biochemical information processing would be any different). This seems to leave only the highly improbable, indefensible, impractical, “chance of the gaps” non-explanation that follows:
- accidental chance events somehow created a lawful program and search space with an exploitable “hill climbing” structure, accidentally generated a replicating information processor (life) and the correct search algorithm to match the exploitable search space structure, and blindly and accidentally caused the necessary problem specific, active information to generate (at a rate far exceeding random chance results by many orders of magnitude) the following features which are known to be causally related to intelligent foresight and intelligent programming/design ...
1. An information processing system which operates off of many layers of algorithmically complex and specified code while evolving further high information content codes (far exceeding 500 bits -- UPB).
2. Repair systems.
3. Logic gates.
4. Complex machinery
5. Complex assembly instructions and pathways.
6. Redundant systems.
8. Intelligent systems.
9. Consistently better than chance performance over separate trials. [Convergent
evolutionary structures and functions] (link to Simon Conway Morris’ book and
the other webpage)
Etc.
However, not to worry, there is a better explanation:
Given the NFL Theorems, COI Theorems, and understanding of CSI, biological evolution is most probably the necessary result of a teleological (end-goal, solution oriented) law, being guided by problem specific information at the foundation of our universe. IOW, evolution is the result of universal laws which have been intelligently fine tuned (programmed) by matching algorithm to problem by incorporating future knowledge of the optimization problem into the evolutionary algorithm to arrive at solutions to problems/targets (some of which are shown by convergent evolution and the other 8 phenomenon/effects listed above).
The basis for this naturalistic teleological hypothesis is scientific since ...
1. It is based on observations (data) of cause and effect for the types of systems in question (information processing systems performing evolutionary optimization, arriving at above listed effects at better than chance performance),
2. It has begun to be tested and can continue to be tested using evolutionary algorithms, evidence being provided with our knowledge of the programming and problem specific information necessary (according to NFL Theorems) for evolutionary algorithms,
3. It is even falsifiable by showing how stochastic, blind, non-teleologically generated processes can cause information processing systems to self organize, generate layers of further “evolvable” coded information, and account for the problem specific information shown to be necessary by the NFL Theorems.
In fact, I don’t see how anyone could get away with not realizing that a naturalistic teleological hypothesis as the cause of life and it’s subsequent evolution is the best scientific explanation, consisting of greatest explanatory power and scope and based on observation of the types of systems in question.
Do you have any better competing hypothesis? If so, please lay it out. Merely critiquing the teleological hypothesis -- that evolution is the necessary cause of a goal oriented procedure which is necessarily shaped by intelligence -- doesn’t automatically make your position (whatever it may be) the correct position.
Again, if you wish to end this discussion, and prove ID Theory wrong and your side (whatever that may be) as right, merely show how a random set of laws will generate an information processing system, problem specific information (thus an evolutionary algorithm), and finally convergent examples of CSI. Based on my understanding and explanation of NFL Theorems and recently developed Conservation of Information Theorems that random generation of information processing systems and evolutionary algorithms is to information theory what perpetual motion free energy machines are to physics and are thus so highly improbable that they are for all practical purposes impossible. Merely show me some data (observation) that a random set of laws will produce the above mentioned set of effects. Or at least show me the theory underpinning such a hypothesis. Data Trumps ... every time. Intelligent Design Theory has played it’s first card ... now it’s the alternative hypothesis’ turn.
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