Evolutionary Computation : A Unified Approach

212 CHAPTER 7. ADVANCED EC TOPICS
some of these terms, but the ideas behind them are quite clear. There are three general
opportunities for adaptation: at EA design time, in between EA runs, and during an EA
run. Each of these issues is briefly discussed in the following subsections.
7.1.1 Adaptation at EA Design Time
At the EA design level, we generally tinker with various design decisions (such as the
selection mechanism, the reproductive operators, population size, etc.) in an attempt to
adapt our design to a particular problem domain. Once those design decisions are made,
the EA is run and, depending on the outcome of one or more runs, may result in redesign.
Since EA design space is quite large, exploring it manually can be quite tedious and time
consuming, providing a strong incentive to automate the design adaptation process. On the
other hand, evaluating the effectiveness of a particular EA design typically involves multiple
EA runs and can be computationally quite expensive. So, a natural reaction is to consider
the use an EA to search EA design space, i.e., a meta-EA.
That, of course, raises the question of the design of the meta-EA! To avoid an infinite
regress, design space exploration at this level is generally a manual process. One of the
earliest examples of this meta-EA approach was Grefenstette (1986) in which various GA
design parameters were optimized for a test suite of optimization problems.
One of the important observations that has come from these meta-EA studies is that
most EAs are relatively insensitive to specific values of design parameters. Rather, there is
a range of values within which acceptable performance is obtained. This robustness allows
many applications to run in “turnkey” mode with a set of default values.
7.1.2 Adaptation over Multiple EA Runs
Since the complexity of the fitness landscape of many difficult application problems is not
well-understood apriori, it is not unusual for an EC practitioner to make a series of EA runs
in which the observations and feedback from initial runs is used to modify the EA design
used in subsequent runs. This can be tedious and time consuming, and raises interesting
questions involving the extent to which this process can be automated by embedding an EA
in an outer loop controlling a sequence of multiple EA runs.
A key issue is the determination of the kind of information that can be extracted from
individual runs and used to improve performance on subsequent runs. One possibility is
to use information acquired about the fitness landscape to bias the direction of search in
subsequent runs. A good example of this approach is described in Beasley et al. (1993)
in which difficult multimodal fitness landscapes are explored by sequentially deforming the
landscape to remove peaks found on earlier runs.
Alternatively, a strategy of having repeated EA restarts using initial populations that are
a combination of individuals from previous runs and randomly generated ones has also been
shown to be quite effective. Perhaps the most striking example of this approach is CHC,
a turnkey optimization package that does not require the user to set any EA parameters
(Eshelman, 1990).
A third possibility is to use performance feedback from earlier EA runs to modify the
parameter values of an EA on subsequent runs. A good example of this approach is the