Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Issues in evolutionary robotics
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
The evolution of mental models
Advances in genetic programming
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
Learning and evolution in neural networks
Adaptive Behavior
Selection for wandering behavior in a small robot
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Concurrent Genetic Programming, Tartarus and Dancing Agents
Proceedings of the Second European Workshop on Genetic Programming
Bio-inspired algorithm for wheeled robot|s navigation
International Journal of Artificial Intelligence and Soft Computing
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In recent years, evolutionary algorithms (EAs) have been successfully used in the design of artificial neural networks for a variety of applications. The suitability of EAs for this design task stems from their ability to adaptively search large spaces in near-optimal ways. One direct application of this advance has been in the area of evolutionary robotics, where EAs are typically used for designing behavior controllers for robots and autonomous agents. While such designs have been found to work well in general, their performance is often limited by the number, placement, quality, efficacy, and reliability of the sensors that the robots are endowed with. In this paper we argue that designing the sensory systems of these robots, in addition to the usual practice of designing the controller, can lead to improvements in the performance of the robot. Our results indicate that the evolution of sensors is a useful enterprise, and can lead to efficient and often counterintuitive controller designs.