Adaptive evolution in static and dynamic environments.
This thesis provides a framework for describing a canonical evolutionary
system. Populations of individuals are envisaged as traversing a search
space structured by genetic and developmental operators under the influence
of selection. Selection acts on individuals' phenotypic expressions, guiding
the population over an evaluation landscape, which describes an idealised
evaluation surface over the phenotypic space. The corresponding valuation
landscape describes evaluations over the genotypic space and may be
transfolIDed by within generation adaptive (learning) or maladaptive (fault
induction) local search.
Populations subjected to particular genetic and selection operators are
claimed to evolve towards a region of the valuation landscape with a
characteristic local ruggedness, as given by the runtime operator correlation
coefficient. This corresponds to the view of evolution discovering an
evolutionarily stable population, or quasi-species, held in a state of dynamic
equilibrium by the operator set and evaluation function. This is
demonstrated by genetic algorithm experiments using the NK landscapes
and a novel, evolvable evaluation function, The Tower of Babel. In
fluctuating environments of varying temporal ruggedness, different operator
sets are correspondingly more or less adapted.
Quantitative genetics analyses of populations in sinusoidally fluctuating
conditions are shown to describe certain well known electronic filters. This
observation suggests the notion of Evolutionary Signal Processing. Genetic
algorithm experiments in which a population tracks a sinusoidally
fluctuating optimum support this view. Using a self-adaptive mutation rate,
it is possible to tune the evolutionary filter to the environmental frequency.
For a time varying frequency, the mutation rate reacts accordingly. With
local search, the valuation landscape is transfolIDed through temporal
smoothing. By coevolving modifier genes for individual learning and the
rate at which the benefits may be d,irectly transmitted to the next generation,
the relative adaptedness of individual learning and cultural inheritance
according to the rate of environmental change is demonstrated.