An evolutionary learning system for synthesizing complex morphological filters.

This paper describes a system based on evolutionary learning, called MORPH, that semi-automates the generation of morphological programs. MORPH maintains a population of morphological programs that is continually enhanced. The first phase of each learning cycle synthesizes morphological sequences that extract novel features which increase the population's diversity. The second phase combines these newly formed operator sequences into larger programs that are better than the individual programs. A stochastic selection process eliminates the poor performers, while the survivors serve as the basis of another learning cycle. Experimental results are presented for binary and grayscale target recognition problems.

The artificial worlds approach to emergent evolution.

Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map that connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but rather emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.

Evolve III: a discrete events model of an evolutionary ecosystem.

Evolve III is a discrete events model of an evolutionary ecosystem. The model includes three levels of organization: population, organism and genetic structure. Each of these components was modeled independently, so that selective replacement of subsystems can be used to create families of models capable of testing alternative hypotheses about the real system. To demonstrate the use of the model we describe an experiment on the relationship between adaptability of populations and the variability of the environment. Populations cultured in a constant environment usually dominated those cultured in a variable environment when both were placed in a variable environment at an early stage of development, whereas the opposite is the case at later stages of development. This agrees with experiments on laboratory microcosms and lends credence to the potential predictive value of the model.

Computational illustration of the bootstrap effect.

A constructive computational model is used to show that even under poor case assumptions genetic systems accumulate features which increase their amenability to evolution. These features decrease the fitness of the individual organism, but hitchhike along with the fitness-increasing traits whose probability of appearance they increase.