Niching methods for genetic algorithms
Niching methods for genetic algorithms
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
High Order Eigentensors as Symbolic Rules in Competitive Learning
Hybrid Neural Systems, revised papers from a workshop
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Voronoi-initializated island models for solving real-coded deceptive problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Linear transformation in Pseudo-Boolean functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exploring Building Blocks through Crossover
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
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A fundamental aspect of many evolutionary approaches to synthesis of complex systems is the need to compose atomic elements into useful higher-level building blocks. However, the ability of genetic algorithms to promote useful building blocks is based critically on genetic linkage - the assumption that functionally related alleles are also arranged compactly on the genome. In many practical problems, linkage is not known a priori or may change dynamically. Here we propose that a problem's Hessian matrix reveals this linkage, and that an eigenstructure analysis of the Hessian provides a transformation of the problem to a space where first-order genetic linkage is optimal. Genetic algorithms that dynamically transforms the problem space can operate much more efficiently. We demonstrate the proposed approach on a real-valued adaptation of Kaufmann's NK landscapes and discuss methods for extending it to higher-order linkage.