An introduction to genetic algorithms
AI Expert
Toward learning robots
Computer
On the effectiveness of genetic search in combinatorial optimization
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Integration, Coordination and Control of Multi-Sensor Robot Systems
Integration, Coordination and Control of Multi-Sensor Robot Systems
A Lower-Bound Result on the Power of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
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A practical implementation of a genetic algorithm for routing a real autonomous robot through a changing environment is described. Moving around in a production plant the robot collects information about its environment and stores it in a temporal map, which is virtually a square grid, taking account of changing obstacles. The evolutional optimizer continuously searches for short paths in this map using string representations of paths as chromosomes. The main features of the implementation include physical realization, random walk exploration, temporal mapping, and dedicated genetic operators.