Mixtures of probabilistic principal component analyzers
Neural Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Analysis of the (1, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Adaptation of expansion rate for real-coded crossovers
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper presents the Real-coded Genetic Algorithms for high-dimensional ill-scaled structures, what is called, the k-tablet structure. The k-tablet structure is the landscape that the scale of the fitness function is different between a k-dimensional subspace and the orthogonal (n-k)-dimensional subspace. The search speed of traditional GAs degrades when a high dimensional k-tablet structure is included in the landscape of the fitness function.In this structure, offspring generated by crossovers are likely to spread wider region than the region where the parental population covers and this causes the stagnation of the search. To resolve this problem, we propose a new crossover LUNDX-m using only m-dimensional latent variables. The effectiveness of the proposal method is tested with several benchmark functions including k-tablet structures and we show that our proposed method performs better than traditional crossovers especially when the dimensionality n is higher than 100.As an example of a k-tablet structure in real world applications, we show that the lens design problem has a kind of k-tablet structures and that our proposed method also performs better than conventional crossovers in this problem.