Evolutionary Computation : A Unified Approach

218 CHAPTER 7. ADVANCED EC TOPICS
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Fitness: Best in Population
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Figure 7.4: EA response to more rapid oscillating change.
of change. Increased diversity can be accomplished in a number of ways. The first and most
obvious strategy is to weaken selection pressure. The difficulty with this approach is getting
the right amount of selection pressure without aprioriknowledge about the dynamics of
the environment. Too much pressure (typical of many static optimizers) results in poor
recovery performance. Too little pressure produces overall mediocre performance.
An alternative is to use some form of crowding (De Jong, 1975) or niching (Goldberg,
1989). Both approaches allow for strong initial selection, but restrict considerably the ability
for individuals to take over the population.
An interesting alternative is to use some form of restricted mating and selection. Island
models and diffusion models restrict selection and mating to local subpopulations, thus
maintaining more diversity. Alternatively, the use of “tag bits” for mating restrictions
permits more dynamically defined “species” (Spears, 1994).
So far, we have focused on maintaining diversity. A different strategy appears to be more
useful when the environment undergoes occasional abrupt changes. In such cases the cost
of maintaining diversity can be quite high. Instead, one can focus on providing diversity
“on demand”. The difficulty here is in recognizing when the need arises without apriori
knowledge about the landscape dynamics. One approach is to monitor and detect significant
environmental changes, and then trigger something like a hypermutation operator (Cobb
and Grefenstette, 1993) to produce the needed diversity. Alternatively, self-tuning operators
such as the “1/5” rule for adapting mutation step size (Bäck and Schwefel, 1993) appear to
be useful.