Intelligence without representation
Artificial Intelligence
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
An Behavior-based Robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Evolutionary computation and structural design: A survey of the state-of-the-art
Computers and Structures
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
An indirect approach to the three-dimensional multi-pipe routing problem
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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A conceptual framework for online evolution in robotic systems called Indirect Online Evolution (IDOE) is presented. A model specie automatically infers models of a physical system and a parameter specie simultaneously optimizes the parameters of the inferred models according to a specified target behavior. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest individual of the parameter specie is executed on the physical system, hence limiting both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE), where every candidate solution has to be evaluated on the physical system. Features of IDOE are demonstrated by inferring models of a simple hidden system containing geometric shapes that are further optimized according to a target value. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of DOE.