Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Using PCA to improve evolutionary cellular automata algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The 2007 IEEE CEC simulated car racing competition
Genetic Programming and Evolvable Machines
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Distributed probabilistic model-building genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
MBEANN: mutation-based evolving artificial neural networks
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Evolutionary computation and games
IEEE Computational Intelligence Magazine
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Bio-inspired computation: success and challenges of IJBIC
International Journal of Bio-Inspired Computation
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Evolutionary learning of neural networks, i.e., neuroevolution, has shown to play an important role in agent constitutions. It has the robustness property for dynamic, practical problems. In the case of a large number of input neurons, however, the search space of neuroevolution becomes much larger so that it is difficult to find out better policies. In this paper, Isomap, one of the manifold learning algorithms, is employed to reduce the dimensionality of the input space. The Isomap tries to reduce the dimensionality based on manifold structures in high dimensional space and to preserve local topological relationships among data. Mario AI is used as a test bed for the proposed method. Video games such as Mario, Ms. Pac-Man, and car racing have been recognised as ideal benchmark problems for computational intelligence, where they require a variety of inputs, real-time strategy, and so on, and they provide good simulators which are capable to apply CI techniques. A large number of scenes in Mario are applied by the Isomap in order to constitute a map from scene information to low dimensional data. Experimental results on the Mario AI show the effectiveness of the proposed method.