机器学习

In many natural systems, individual organisms learn to adapt significantly during their lifetime.
At the same time, biological and social processes allow their species to adapt over a time frame of many
generations. One interesting question regarding evolutionary systems is "What is the relationship
between learning during the lifetime of a single individual, and the longer time frame species-level
learning afforded by evolution?'
Lamarckian Evolution
Larnarck was a scientist who, in the late nineteenth century, proposed that evolution over many generations was directly influenced by  the expriences of individual organisms during their lifetime.in particular, he proposed that experiences of a single organism directly affected the genetic makeup of
their offspring: If an individual learned during its lifetime to avoid some toxic food, it could pass this
trait on genetically to its offspring, which therefore would not need to learn the trait. This is an
attractive conjecture, because it would presumably allow for more efficient evolutionary progress than
a generate-and-test process (like that of GAS and GPs) that ignores the experience gained during an
individual's lifetime. Despite the attractiveness of this theory, current scientific evidence
overwhelmingly contradicts Lamarck's model. The currently accepted view is that the genetic makeup
of an individual is, in fact, unaffected by the lifetime experience of one's biological parents. Despite this
apparent biological fact, recent computer studies have shown that Lamarckian processes can
sometimes improve the effectiveness of computerized genetic algorithms (see Grefenstette 1991;
Ackley and Littman 1994; and Hart and Belew 1995).
Baldwin Effect
Although Lamarckian evolution is not an accepted model of biological evolution, other mechanisms
have been suggested by which individual learning can alter the course of evolution. One such
mechanism is called the Baldwin effect, after J. M. Baldwin (1896), who first suggested the idea. The
Baldwin effect is based on the following observations:
 If a species is evolving in a changing environment, there will be evolutionary pressure to favor
individuals with the capability to learn during their lifetime. For example, if a new predator
appears in the environment, then individuals capable of learning to avoid the predator will be
more successful than individuals who cannot learn. In effect, the ability to learn allows an
individual to perform a small local search during its lifetime to maximize its fitness. In contrast,
nonlearning individuals whose fitness is fully determined by their genetic makeup will operate
at a relative disadvantage.
 Those individuals who are able to learn many traits will rely less strongly on their genetic code
to "hard-wire" traits. As a result, these individuals can support a more diverse gene pool, relying
on individual learning to overcome the "missing" or "not quite optimized" traits in the genetic
code. This more diverse gene pool can, in turn, support more rapid evolutionary adaptation.
Thus, the ability of individuals to learn can have an indirect accelerating effect on the rate of
evolutionary adaptation for the entire population.
To illustrate, imagine some new change in the environment of some species, such as a new
predator. Such a change will selectively favor individuals capable of learning to avoid the predator. As
the proportion of such self-improving individuals in the population grows, the population will be able to
support a more diverse gene pool, allowing evolutionary processes (even non-Lamarckian generate-
and-test processes) to adapt more rapidly. This accelerated adaptation may in turn enable standard
evolutionary processes to more quickly evolve a genetic (nonlearned) trait to avoid the predator (e.g.,
an instinctive fear of this animal). Thus, the Baldwin effect provides an indirect mechanism for
individual learning to positively impact the rate of evolutionary progress. By increasing survivability and
genetic diversity of the species, individual learning supports more rapid evolutionary progress, thereby
increasing the chance that the species will evolve genetic, nonlearned traits that better fit the new
environment.
There have been several attempts to develop computational models to study the Baldwin
effect. For example, Hinton and Nowlan (1987) experimented with evolving a population of simple
neural networks, in which some network weights were fixed during the individual network "lifetime,"
while others were trainable. The genetic makeup of the individual determined which weights were
114
trainable and which were fixed. In their experiments, when no individual learning was allowed, the
population failed to improve its fitness over time. However, when individual learning was allowed, the
population quickly improved its fitness. During early generations of evolution the population contained
a greater proportion of individuals with many trainable weights. However, as evolution proceeded, the
number of fixed, correct network weights tended to increase, as the population evolved toward
genetically given weight values and toward less dependence on individual learning of weights.
Additional computational studies of the Baldwin effect have been reported by Belew (1990), Harvey
(1993), and French and Messinger (1994). An excellent overview of this topic can be found in Mitchell
(1996). A special issue of the journal Evolutionary Computation on this topic (Turney et al. 1997)
contains several articles on the Baldwin effect.