I thought the meaning of ‘explicit’ was clear. If you look at the code of Weasel, you’ll find the target string. Even in Weasels that let you type in the target, it is in memory.

But there is no target design in the antenna example. There is only measuring efficiency, and ranking that against other designs in the population.

So the point of my post was that there are interesting problems that are more complicated than hill climbing smoothly towards a fixed target, and evolutionary algorithms can still solve them, contra a dismissive wave of the hand.

With respect to antenna design, this particlar group of researchers was either interested in building better antennas, or thought antenna design was a hard problem for humans, and therefore a good test problem for GP. Other research is not interested in getting useful results, but simply in understanding the limits of EAs. I’m sure there is an ‘edge of evolution’, and books like David Goldberg’s ‘Design of Innovation’ explore it.

Not sure how you are using the term “explicit,” however as per the antenna example there definitely is a target. That target is an efficient antenna. In this case, the target was a specific function instead of a form. The programmers knew what function they wished to achieve and programmed the constraints to achieve that function and without that function of an efficient antenna the form (the exact shape of the antenna) wouldn’t have been discovered.

The point is that absent the foresight of the programmers to achieve a specific end function, there would be no ‘some results.’

I have been busy elsewhere on other matters for the past week or so.

I came back by to see where the thread went.

SA has put his finger on the key issue: the ORIGIN of functional complex, specific information is what has to be accounted for. And, both Weasel and the more modern GA’s do not address that.

In effect they start within the shores of an island of function, without first credibly getting us to those shores in a very large config space well beyond the scanning ability of he resources of the atoms of the observed cosmos. remember, that starts at 500 – 1,000 bits as a rule of thumb.

To see the force of that, think about the requisites for a von Neumann self-replicator:

1 –> A code system, with symbols and combinational rules that specify meaningful and functional as opposed to “nonsense” strings. [Situations where every combination has a function are irrelevant.]

2 –> A storage unit with blueprint or tape mechanism that encodes the specifications and at least implies the assembly instructions

3 –> A reader that then drives associated implementation machines that actually carry out the replication.

4 –> A source of required parts (i.e. a pre existing reservoir and/or a metabilic subsystem to make parts out of easily accessible environmental resources)

This is an irreducibly complex set of core elements, i.e, remove any one and self-replicational functionality vanishes. It also specifies an island of functional organisaiton, as not just any combination of any and all generic parts will achieve the relevant function.

That is why the randomly varied “genes” in a GA string are irrelevant. For, absent the independent reader and translator into action, the strings have no function. And, the process of reading and converting into a functional behaviour and/or metric is plainly intelligently designed in all cases of GA’s on record.

We could go on and on, but the point is plain enough.

GEM of TKI

They must do nothing less to lend any support to the hypothesis of increased complexity via RM+NS. Otherwise they’re a parlor trick. (Or an easier way of designing better antenna surfaces.)

The “hypothesis of increased complexity” is a term exclusive to the mode of thinking upon which Intelligent Design is based and is irrelevant with respect to fitness adaptation, microevolution.

The point of getting ‘some results’ is that it happens without an explicit target, contra what many here and elsewhere are saying is necessary. That consistent misunderstnding of the necessity of targets to EAs has been the genesis of much discussion here!

I’d be really curious to see if any of these “evolved” antennas, on their own, achieved any sort of innovation, such as motors to orient themselves toward a signal, circuitry to enhance the signal, or some relays.

They must do nothing less to lend any support to the hypothesis of increased complexity via RM+NS. Otherwise they’re a parlor trick. (Or an easier way of designing better antenna surfaces.)

” and (ii) the key begged question, again is to get to shores of complex functionality sufficient for further hill climbing to be relevant,….”

This is an important issue, that you raise several times in your post. It is important because it represents a fundamental misconception about evolutionary theory.

Accepting for the sake of argument that “shores of complex functionality” actually exist, there is no need for evolutionary mechanisms to find them. Living creatures that reproduce already have a successful genome. Evolutionary mechanisms, such as those simulated in programs like ev, don’t need to find a viable point in genome space — they’re already at one and are simply exploring nearby points.

Abiogenesis is an interesting topic, but it is distinct from evolutionary theory.

Given this, the rest of your response does not address the core question. Where, exactly, does the “active information” get injected into ev?

“5] “The amount of information in the genomes of the final population is much higher than that in the initial population, using only simple evolutionary mechanisms.”

Again, Schneider’s Ev is discussing a pre-programmed context that assigns functions, sets up hill-climbing algorithms and gives particular meaning to digital strings according to certain symbol and rule conventions,…”

You need to read the paper more carefully. Schneider’s ev is a simulation of a subset of known evolutionary mechanisms applied to a known biological system. The only “meaning” assigned to any digital strings is that which reflects real world chemistry. With even a very simple set of mechanisms, Schneider demonstrated the ability to evolve significant amounts of information. Equally importantly, his simulation results are consistent with the empirical evidence resulting from his research on real biological systems. That’s very strong support for the ability of evolutionary mechanisms to transfer information from an environment to subsequent populations.

“… and inter alia measures Shannon information, which — as a metric of info-carrying or storing capacity — is irrelevant to the issue of origin of algorithmically functional, complex specified information in a context of first life or novel body plans.”

Shannon information is a standard, well-understood metric. Schneider explains how and why it is appropriate in his thesis. After a quick re-review of that thesis, I suspect that any rigorously defined, objective, quantitative measure of information could be used. The fact is that the amount of information in the sequence patterns at a binding site evolves to be equal to the amount of information required to locate the number of such sites within the genome.

“Remember, too (as was already pointed out but ignored): Shannon information for a given symbol string length peaks for non-functional flat random code,….”

That is immaterial in this context. If you read the ev paper and Schneider’s thesis, you will see that the important measurement is the relationship between the amount of information in a binding site sequence and the amount of information required to locate a binding site.

“6] “If you read the paper, you’ll see that the fitness landscape itself is constantly changing.”

Irrelevant: (i) the “fitness landscape” is MAPPED and ALGORITHMICALLY PROCESSED at any given time (to get the hill-climbing by differential fitness metric values),…”

No, it is not. Read the thesis.

“7] “ev does it”

Ev does not create its global algorithmic functionality ab initio from undirected chance plus necessity, but from an intelligent programmer.”

You are again mistaking what is being simulated. ev shows that a small subset of known evolutionary mechanisms is sufficient to transfer information from the environment to subsequent populations, without any need for intelligent intervention.

“3] “Life ‘knows’ the target, it is ‘aware’ of the target, i.e. it detects when it is pointing closer to or farther from the ‘target’, i.e. increasing or decreasing in fitness.”

See the point?

The issue is not to improve already functioning life forms and body plans, but to first get to them, in light of the entailed complex, functionally specific information basis for such life.”

That is the issue for theories of abiogenesis. It is not the issue for evolutionary theory. Evolutionary theory explains how populations change over time, given the existence of self-replicating entities.

“Antenna theory and Genetic Algorithms used to design novel antennas, are based on a deeply established theory of the function of such antennas {based on Maxwell’s Electromagnetism], programmed into the simulation by its designers.

And, that is the precise source of the relevant active information.”

It’s important to be clear on exactly what is being simulated in these types of genetic algorithms. Typically there are two primary components: a population generator and a fitness evaluator. In the case of the antenna GA, the fitness evaluator uses standard, real world physics to determine the performance of the design represented by each member of the current population. The laws of physics themselves are not being simulated.

The population generator implements a subset of known evolutionary mechanisms. At a minimum, the likelihood of a particular gene making it into the next generation will be related to the fitness of the individuals in the current population with that gene (stochastically, in some selection algorithms). Some type of mutation is also required. Other mechanisms such as cross-over may be used. The simulation, therefore, is of the evolutionary mechanisms themselves.

Claiming that the laws of physics are providing the “active information” is, as I noted previously, equivalent to recognizing that the evolutionary mechanisms being simulated are capable of transferring information about the environment to subsequent population. Again, this is what we observe in actual biological systems, with no intelligent intervention required.

I’ll respond to some of your other points separately in the interests of keeping each post readable.