Evolutionary Computation Glossary
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(or an elitist strategy) is a mechanism which is employed in some
which ensures that the
of the most highly fit member(s) of the
are passed on to the next
without being altered by
Using elitism ensures that the minimum
of the population can never reduce from one generation to the next. Elitism usually brings about a
more rapid convergence of the population. In some applications elitism improves the chances of
locating an optimal
while in others it reduces it.
The EvolutioNary Computation REpository Network. An
servers/World Wide Web sites holding all manner of interesting things related to
See Q15.3 for more information.
(biol) That which surrounds an organism. Can be
'physical' (abiotic), or biotic. In both, the organism occupies a
which influences its
within the total
A biotic environment may present frequency-dependent fitness functions within a
that is, the fitness of an organism's behaviour may depend upon how many others are also doing it.
biotic environments may foster co-evolution, in which fitness is determined with
partly by other
(biol) A "masking" or "switching" effect among
A biology textbook says: "A gene is said to be epistatic when its presence suppresses the effect of
a gene at another locus. Epistatic genes are sometimes called inhibiting genes because of their
effect on other genes which are described as hypostatic."
When EC researchers use the term
they are generally referring to any kind of strong interaction among genes, not just masking
effects. A possible definition is:
Epistasis is the interaction between different genes in a
It is the extent to which the contribution to
of one gene depends on the values of other genes.
Problems with little or no epistasis are trivial to solve (hillclimbing is sufficient). But highly
epistatic problems are difficult to solve, even for
High epistasis means that
cannot form, and there will be
That process of change which is assured given a
in which there are (1) varieties of
with some varieties being (2) heritable, of which some varieties (3) differ in
(reproductive success). (See the talk.origins
for discussion on this (See Q10.7).)
"Don't assume that all people who accept
--- Talk.origins FAQ
A type of
developed in the early 1960s in Germany. It employs real-coded parameters, and in its original
form, it relied on
as the search operator, and a
size of one. Since then it has evolved to share many features with
See Q1.3 for more information.
that does well in a
dominated by the same strategy. (cf Maynard Smith, 1974) Or, in other words, "An 'ESS' ... is a
strategy such that, if all the members of a population adopt it, no mutant strategy can invade."
(Maynard Smith "Evolution and the Theory of Games", 1982).
A algorithm designed to
Encompasses methods of
on a computer. The term is relatively new and represents an effort bring together researchers who
have been working in closely related fields but following different paradigms. The field is now
seen as including research in
and so forth. For a good overview see the editorial introduction to Vol. 1, No. 1 of
"Evolutionary Computation" (MIT Press, 1993). That, along with the papers in the issue,
should give you a good idea of representative research.
algorithm developed in the mid 1960s. It is a stochastic
strategy, which is similar to
but dispenses with both "genomic" representations and with
See Q1.2 for more information.
A process or system which
employs the evolutionary dynamics of
The specific forms of these processes are irrelevant to a system being described as "evolutionary."
Or expected value. Pertaining to a random variable
X, for a continuous random variable, the expected value is:
E(X) = INTEGRAL(-inf, inf) [X f(X) dX].
The discrete expectation takes a similar form using a summation instead of an integral.
When traversing a
is the process of using information gathered from previously visited points in the search space to
determine which places might be profitable to visit next. An example is hillclimbing, which
investigates adjacent points in the search space, and moves in the direction giving the greatest
Exploitation techniques are good at finding local maxima.
The process of visiting entirely new regions of a
to see if anything promising may be found there. Unlike
involves leaps into the unknown. Problems which have many local maxima can sometimes only be solved
by this sort of random search.
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Hitch Hiker's Guide to Evolutionary Computation,
Issue 9.1, released 12 April 2001
Copyright © 1993-2001 by J. Heitkötter and
D. Beasley, all rights reserved.