David Ackley is a researcher of neural nets and genetic algorithms
at Bellcore, the R&D labs for the Baby Bells. Ackley has some of the
most original ways of looking at evolutionary systems that I've come
Ackley is a bear of a guy with a
side-of-the-mouth wisecracking delivery. He broke up 250 serious
scientists at the 1990 Second Artificial Life Conference with a wickedly
funny video of a rather important artificial life world he and colleague
Michael Littman had made. His "creatures" were actually bits of code not
too different from a classical GA, but he dressed them up with moronic
smiley faces as they went about chomping each other or bumping into
walls in his graphical world. The smart survived, the dumb died. As
others had, Ackley found that his world was able to evolve amazingly fit
organisms. Successful individuals would live Methuselahian
lifetimes -- 25,000 day-steps in his world. These guys had the system all
figured out. They knew how to get what they needed with minimum effort.
And how to stay out of trouble. Not only would individuals live long,
but the populations that shared their genes would survive eons as
Noodling around with the genes of these streetwise creatures, Ackley
uncovered a couple of resources they hadn't taken up. He saw that he
could improve their chromosomes in a godlike way to exploit these
resources, making them even better adapted to the environment he had set
up for them. So in an early act of virtual genetic engineering, he
modified their evolved code and set them back again into his world. As
individuals, they were superbly fitted and flourished easily, scoring
higher on the fitness scale than any creatures before them.
But Ackley noticed that their population numbers were always lower than
the naturally evolved guys. As a group they were anemic. Although they
never died out, they were always endangered. Ackley felt their low
numbers wouldn't permit the species to last more than 300 generations.
So while handcrafted genes suited individuals to the max, they lacked
the robustness of organically grown genes, which suited the species to
the max. Here, in the home-brewed world of a midnight hacker, was the
first bit of testable proof for hoary ecological wisdom: that what is
best for an individual ain't necessarily best for the species.
"It's tough accepting that we can't figure out what's best in the long
run," Ackley told the Artificial Life conference to great applause,
"but, hey, I guess that's life!"
Bellcore allowed Ackley to pursue his microgod world because they
recognized that evolution is a type of computation. Bellcore was, and
still is, interested in better computational methods, particularly those
based on distributed models, because ultimately a telephone network is a
distributed computer. If evolution is a useful type of distributed
computation, what might some other methods be? And what improvements or
variations, if any, can we make to evolutionary techniques? Taking up
the usual library/space metaphor, Ackley gushes, "The space of
computational machinery is unbelievably vast and we have only explored
very tiny corners of it. What I'm doing, and what I want to do more of,
is to expand the space of what people recognize as computation."
Of all the possible types of computation, Ackley is primarily interested
in those procedures that underpin learning. Strong learning methods
require smart teachers; that's one type of learning. A smart teacher
tells a learner what it should know, and the learner analyzes the
information and stores it in memory. A less smart teacher can also teach
by using a different method. It doesn't know the material itself, but it
can tell when the learner guesses the right answer -- as a substitute
teacher might grade tests. If the learner guesses a partial answer the
weak teacher can give a hint of "getting warm," or "getting cold" to
help the learner along. In this way, a weaker teacher can potentially
generate information that it itself doesn't own. Ackley has been pushing
the edge of weak learning as a way of maximizing computation: leveraging
the smallest amount of information in, to get the maximum information
out. "I'm trying to come up with the dumbest, least informative teacher
as possible," Ackley told me. "And I think I found it. My answer is:
Death is the only teacher in evolution. Ackley's mission was to find
out: what can you learn using only death as a teacher? We don't know for
sure, but some candidates are: soaring eagles, or pigeon navigation
systems, or termite skyscrapers. It takes a while, but evolution is
clever. Yet it is obviously blind and dumb. "I can't imagine any dumber
type of learning than natural selection," says Ackley.
In the space of all possible computation and learning, then, natural
selection holds a special position. It occupies the extreme point where
information transfer is minimized. It forms the lowest baseline of
learning and smartness, below which learning doesn't happen and above
which smarter, more complicated learning takes place. Even though we
still do not fully understand the nature of natural selection in
coevolutionary worlds, natural selection remains the elemental melting
point of learning. If we could measure degrees of evolution (we can't
yet) we would have a starting benchmark against which to rate other
types of learning.
Natural selection plays itself out in many guises. Ackley was right;
computer scientists now realize that many modes of computation
exist -- many of them evolutionary. For all anyone knows, there may be
hundreds of styles of evolution and learning. All such strategies,
however, perform a search routine through a library or space.
"Discovering the notion of the 'search' was the one and only brilliant
idea that traditional AI research ever had," claims Ackley. A search can
be accomplished in many ways. Natural selection -- as it is run in organic
life -- is but one flavor.
Biological life is wedded to a particular hardware: carbon-based DNA
molecules. This hardware limits the versions of
search-by-natural-selection that can successfully operate upon it. With
the new hardware of computers, particularly parallel computers, a host
of other adaptive systems can be conjured up, and entirely different
search strategies set out to shape them. For instance, a chromosome of
biological DNA cannot broadcast its code to DNA molecules in other
organisms in order for them to receive the message and alter their code.
But in a computer environment you can do that.
David Ackley and Michael Littman, both of Bellcore's Cognitive Science
Research Group, set out to fabricate a non-Darwinian evolutionary system
in a computer. They chose a most logical alternative: Lamarckian
evolution -- the inheritance of acquired traits. Lamarckism is very
appealing. Intuitively such a system would seem deeply advantageous over
the Darwinian version, because presumably useful mutations would be
adopted into the gene line more quickly. But a look at its severe
computational requirements quickly convinces the hopeful engineer how
unlikely such a system would be in real life.
If a blacksmith acquires bulging biceps, how does his body reverse-
engineer the exact changes in his genes needed to produce this
improvement? The drawback for a Lamarckian system is its need to trace a
particular advantageous change in the body back through embryonic
development into the genetic blueprints. Since any change in an
organism's form may be caused by more than one gene, or by many
instructions interacting during the body's convoluted development,
unraveling the tangled web of causes of any outward form requires a
tracking system almost as complex as the body itself. Biological
Lamarckian evolution is hampered by a strict mathematical law: that it
is supremely easy to multiply prime factors together, but supremely hard
to derive the prime factors out of the result. The best encryption
schemes work on this same asymmetrical difficulty. Biological Lamarckism
probably hasn't happened because it requires an improbable biological
But computational entities don't require bodies. In computer evolution
(as in Tom Ray's electric-powered evolution machine) the computer code
doubles as both gene and body. Thus, the dilemma of deriving a genotype
from the phenotype is moot. (The restriction of monolithic
representation is not all that artificial. Life on Earth must have
passed through this stage, and perhaps any spontaneously organizing
vivisystem must begin with a genotype that is restricted to its
phenotype, as simple self-replicating molecules would be.)
In artificial computer worlds, Lamarckian evolution works. Ackley and
Littman implemented a Lamarckian system on a parallel computer with
16,000 processors. Each processor held a subpopulation of 64
individuals, for a grand total of approximately one million individuals.
To simulate the dual information lines of body and gene, the system made
a copy of the gene for each individual and called the copy the "body."
Each body was a slightly different bit of code trying to solve the same
problem as its million siblings.
The Bellcore scientists set up two runs. In the Darwinian run, the body
code would mutate over time. By chance a lucky guy might become code
that provides a better solution, so the system chooses it to mate and
replicate. But in Darwinism when it mates, it must use its original
"gene" copy of the code -- the code it inherited, not the improved body
code it acquired during its lifetime. This is the biological way; when
the blacksmith mates, he uses the code for the body he inherited, not
the body he acquired.
In the Lamarckian run, by contrast, when the lucky guy with the improved
body code is chosen to mate, it can use the improved code acquired
during its lifetime as the basis for its mating. It is as if a
blacksmith could pass on his massive arms to his offspring.
Comparing the two systems, Ackley and Littman found that, at least for
the complicated problems they looked at, the Lamarckian system
discovered solutions almost twice as good as the Darwinian method. The
smartest Lamarckian individual was far smarter than the smartest
Darwinian one. The thing about Lamarckian evolution, says Ackley, is
that it "very quickly squeezes out the idiots" in a population. Ackley
once bellowed to a roomful of scientists, "Lamarck just blows the doors
off of Darwin!"
In a mathematical sense, Lamarckian evolution injects a bit of learning
into the soup. Learning is defined as adaptation within an individual's
lifetime. In classical Darwinian evolution, individual learning doesn't
count for much. But Lamarckian evolution permits information acquired
during a lifetime (including how to build muscles or solve equations) to
be incorporated into the long-term, dumb learning that takes place over
evolution. Lamarckian evolution produces smarter answers because it is a
smarter type of search.
The superiority of Lamarckism surprised Ackley because he felt that
nature did things so well: "From a computer science viewpoint it seems
really stupid that nature is Darwinian and not Lamarckian. But nature is
stuck on chemicals. We're not." It got him thinking about other types of
evolution and search methods that might be more useful if you weren't
restricted to operating on molecules.