Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter or measured a posteriori by estimations based on population size or reproductive rates. These methods do not capture the role of environmental complexity in shaping the selective pressures that control the adaptive process. Ecological simulations enabled by computational tools such as the latent energy environment (LEE) model allow us to characterize more closely the effects of environmental complexity on the evolution of adaptive behaviors. LEE is described in this article. Motivation for the development of the LEE model arises from the need to vary complexity in controlled and predictable ways, without assuming the relationship of these changes to the adaptive behaviors they engender. This goal is achieved through a careful characterization of environments in which different forms of "energy" are well defined. A genetic algorithm using endogenous fitness, and local selection is used to model the evolutionary process. Individuals in the population are modeled by neural networks with simple sensorimotor systems, and variations in their behaviors are related to interactions with varying environments. We outline the results of three experiments that analyze different sources of environmental complexity and their effects on the collective behaviors of evolving populations.
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