Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
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
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
Simbad: an autonomous robot simulation package for education and research
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Neuroevolution with manifold learning for playing Mario
International Journal of Bio-Inspired Computation
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Manifold Learning has attracted much attention for this decade. One of the main features of Manifold Learning is that Manifold Learning tries to conserve local topologies in high-dimensional space. In this paper, we discuss the effect of the dimensionality reduction of input spaces of Evolutionary Learning. We examine two Manifold Learning algorithms: Isomap and LLE. We adopt the Instance-Based Policy Optimization as an Evolutionary Learner. In addition, we introduce a metric of relative error of distances between original input space and reduced space. We will show the relationship between this metric and the number of neighbors in Manifold Learning.