Evolving visually guided robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
A Robust Layered Control System For a Mobile Robot
A Robust Layered Control System For a Mobile Robot
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The use of evolutionary methods to generate controllers for real-world autonomous agents has attracted recent attention. Most of the pertinent research has employed genetic algorithms or variations thereof. Recent research has applied an alternative evolutionary method, evolution strategies, to the generation of simple Braitenberg vehicles. This application accelerates the development of such controllers by more than an order of magnitude (a few hours compared to more than two days). Motivated by this useful speedup, this paper investigates the evolution of more complex architectures, receptive-field controllers, that can employ nonlinear interactions and, therefore, can yield more complex behavior. It is interesting to note that the evolution strategy yields the same efficacy in terms of function evaluations, even though the second class of controllers requires up to 10 times more parameters than the simple Braitenberg architecture. In addition to the speedup, there is an important theoretical reason for preferring an evolution strategy over a genetic algorithm for this problem, namely the presence of epistasis.