A Neuro-Ethological Approach for the TSP: Changing Metaphors in Connectionist Models

Biological systems often offer solutions to difficult problems which are not only original but also efficient. Connectionist models have been inspired by neural systems and successfully applied to the formulation of algorithms for solving complex problems such as the traveling salesman problem. In this paper we extend the connectionist metaphor to include an ethological account of how problems similar to the traveling salesman problem are solved by real living systems. A model is presented in which a population of neural networks with simple sensory-motor systems evolve genetically in simulated environments which represent the problem instances to be solved. Preliminary results are discussed, showing how the ethological metaphor allows to overcome some shortcomings of other connectionist models, such as their time and space complexity.