Grand tour methods: an outline
Proceedings of the Seventeenth Symposium on the interface of computer sciences and statistics on Computer science and statistics
The visual display of quantitative information
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Adaptation in natural and artificial systems
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An introduction to genetic algorithms
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Introduction to Switching Theory and Logical Design
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Visualizing Multivariate Functions, Data, and Distributions
IEEE Computer Graphics and Applications
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VIS '90 Proceedings of the 1st conference on Visualization '90
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Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A P2P genetic algorithm environment for the internet
Communications of the ACM - Transforming China
Hyperspace Geography: Visualizing Fitness Landscapes beyond 4D
Artificial Life
An analysis of the solution quality of the simple genetic algorithm
BICA'12 Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
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In this paper we present a technique for visualising hierarchical and symmetric, multimodal fitness functions that have been investigated in the evolutionary computation literature. The focus of this technique is on landscapes in moderate-dimensional, binary spaces (i.e., fitness functions defined over {0,1}n, for n ≤ 16). The visualisation approach involves an unfolding of the hyperspace into a two-dimensional graph, whose layout represents the topology of the space using a recursive relationship, and whose shading defines the shape of the cost surface defined on the space. Using this technique we present case-study explorations of three fitness functions: royal road, hierarchical-if-and-only-if (H-IFF), and hierarchically decomposable functions (HDF). The visualisation approach provides an insight into the properties of these functions, particularly with respect to the size and shape of the basins of attraction around each of the local optima.