Sammon's mapping using neural networks: a comparison
Pattern Recognition Letters - special issue on pattern recognition in practice V
Search space boundary extension method in real-coded genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
A Survey And Analysis Of Diversity Measures In Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Self-Adaptive Particle Swarm Optimization Algorithm with Individual Coefficients Adjustment
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
New usage of Sammon's mapping for genetic visualization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
GAVEL - a new tool for genetic algorithm visualization
IEEE Transactions on Evolutionary Computation
Tracer spectrum: a visualisation method for distributed evolutionary computation
Genetic Programming and Evolvable Machines
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
dPSO-vis: topology-based visualization of discrete particle swarm optimization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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It is a hard problem to understand the search process of particle swarm optimization over high-dimensional domain. The visualization depicts the total search process and then it will allow better understanding of how to tune the algorithm. For the investigation, we adopt Sammon's mapping, which is a well-known distance-preserving mapping. We demonstrate the usefulness of the proposed methodology by applying it to some function optimization problems.