Biological Cybernetics
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
Evolving neural networks through augmenting topologies
Evolutionary Computation
Generating Equations with Genetic Programming for Control of a Movable Inverted Pendulum
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
A Taxonomy for artificial embryogeny
Artificial Life
Advances in evolutionary computing
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Accelerating neuroevolutionary methods using a Kalman filter
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Exploiting functional relationships in musical composition
Connection Science - Music, Brain, Cognition
Real-time evolution of neural networks in the NERO video game
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Neuroevolution strategies for episodic reinforcement learning
Journal of Algorithms
Interactive evolution of particle systems for computer graphics and animation
IEEE Transactions on Evolutionary Computation
Evolving content in the galactic arms race video game
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Scaffolding for interactively evolving novel drum tracks for existing songs
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Autonomous Agents and Multi-Agent Systems
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
On the deleterious effects of a priori objectives on evolution and representation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Sequential constant size compressors for reinforcement learning
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Controlling unstable nonlinear systems with neural networks can be problematic. Two examples presented here show that evolutionary programming provides a feasible method for addressing such control problems.The successful application of classic control design techniques usually requires an extensive knowledge of the system to be controlled, including an accurate model of its dynamics. In some situations, this information may be difficult or even impossible to obtain. The challenge is to control a system without a priori information about its dynamics.One way to achieve this is to evolve neural networks to control the system using only sparse feedback from the system. Such neural networks can be evolved by using evolutionary programming, a member of the class of stochastic optimization techniques commonly described as evolutionary computation. Evolutionary programming has been successfully used to optimize the performance of neural networks by evolving their weights and biases.Neural networks are usually trained by an algorithm called the generalized delta rule, which computes derivatives of the error surface with respect to the weight changes by a simple application of the chain rule called backpropagation. The evolutionary programming approach to training neural networks does not require the calculation of any derivatives, and is therefore potentially useful in problem domains where such information is unavailable or might be difficult or computationally costly to obtain. The design of neurocontrollers for dynamic, nonlinear, unstable systems without a priori knowledge of the dynamics of the system is one such domain. It is this class of problems to which the current study applies evolutionary programming.More specifically, the problems we consider here involve the balancing of two poles on a moving cart, with the poles being either separated or jointed. The objective is to keep the poles upright and the cart within specified limits by simply pushing or pulling the cart. Evolutionary programming trains a feedforward neural network using only sparse feedback from the environment concerning its performance. By sparse we mean that the only feedback information available is the failure signal when either of the poles exceeds a maximum angle of deflection or when the cart reaches the end of its track.