Evolution of neural control structures: Some experiments on mobile robots

From perception to action and form action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness of incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is very important limitation of the evolutionary approach

Simulating Species Richness Using Agents with Evolving Niches, with an Example of Galápagos Plants

I sought to evolve plant species richness patterns on 22 Galápagos Islands, Ecuador, as an exploration of the utility of evolutionary computation and an agent-based approach in biogeography research. The simulation was spatially explicit, where agents were plant monocultures defined by three niche dimensions, lava (yes or no), elevation, and slope. Niches were represented as standard normal curves subjected to selection pressure, where neighboring plants bred if their niches overlapped sufficiently, and were considered the same species, otherwise they were different species. Plants that bred produced seeds with mutated niches. Seeds dispersed locally and longer distances, and established if the habitat was appropriate given the seed's niche. From a single species colonizing a random location, hundreds of species evolved to fill the islands. Evolved plant species richness agreed very well with observed plant species richness. I review potential uses of an agent-based representation of evolving niches in biogeography research

Evolution of Food Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming

This paper describes the recently developed genetic programming paradigm which genetically breeds a population of computer programs to solve problems. The paper then shows, step by step, how to apply genetic programming to a problem of behavioral ecology in biology -- specifically, two versions of the problem of finding an optimal food foraging strategy for the Caribbean Anolis lizard. A simulation of the adaptive behavior of the lizard is required to evaluate each possible adaptive control strategy considered for the lizard. The foraging strategy produced by genetic programming is close to the mathematical solution for the one version for which the solution is known and appears to be a reasonable approximation to the solution for the second version of the problem. Acknowledgments The collaborative work contained in this paper is the outgrowth of the Founding Workshop in Adaptive Computation held at the Santa Fe Institute on March 10-15, 1992. This paper has been submitted to the Si..