Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Machine Learning - Special issue on inductive transfer
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Evolutionary computation
An Behavior-based Robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Computing in Design and Manufacture V
Adaptive Computing in Design and Manufacture V
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Robustness of Case-Initialized Genetic Algorithms
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Combining Control Strategies Using Genetic Algorithms with Memory
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Resilient Individuals Improve Evolutionary Search
Artificial Life
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In task control, evolutionary optimization tends to favor controllers that solve the easier task instances but that fail to solve the harder ones. We call this the problem of hard instances. The doping-driven evolutionary control algorithm (DECA) is introduced to deal with the problem. The effectiveness of DECA is assessed on two task-control problems: a box-pushing task and a food-gathering task. The experimental results show DECA to generate controllers that can solve both the easy and hard instances of both task-control problems. We discuss the results by offering a qualitative explanation for DECA's success and comparing it to related techniques. We conclude that the problem of hard instances is alleviated by the application of DECA.