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**Subsections**

##

2.4 Other GBML Models

This section covers some models which are orthogonal to those
discussed earlier.

####

Online Evolutionary Computation

In many problems, especially sequential ones, feedback is very noisy
and needs averaging. Whiteson and Stone [293] allocated
trials to chromosomes in proportion to their fitness with the
following procedure. At each new generation evaluate each chromosome
once only. Allocate subsequent evaluations using a softmax
distribution based on the initial fitnesses and recalculate the
average fitness of a chromosome after each evaluation. In
non-stationary problems a recency-weighted average of fitness samples
is used. They call this approach *online Evolutionary
Computation*. Its advantages are that less time is wasted evaluating
weaker chromosomes, and, in cases where mistakes matter, fewer
mistakes are made by agents during fitness evaluations. However, the
improvement is only on average; worst-case performance is not
improved. This is related to other work on optimising noisy fitness
functions [262,20], except that they do not
reduce online mistakes.

####

Steady State EAs

Whereas standard generational EAs replace the entire population each
generation, steady-state EAs replace a subset (e.g. only two in
XCS). This approach is standard in Michigan LCS because they minimise
disruption to the population, which is useful for on-line
learning. Steady-state EAs introduce selection for deletion as well as
reproduction and this is typically biased toward lower fitness
chromosomes or to reduce crowding.

####

Co-evolving Learners and Problems

Another possibility not mentioned in our earlier classifications is to
co-evolve both learners and problems. When successful, this allows
learners to gradually solve harder problems rather than tackling the
most difficult problems from the start. It also allows us to search
the space of problems to find those which are harder for a given
learner, and to explore the dynamics between learners and problems.

** Next:** GBML Areas
** Up:** A Framework for GBML
** Previous:** The Interaction of Learning
** Contents**
T Kovacs
2011-03-12