The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Future Generation Computer Systems
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Optimizing Hidden Markov Models with a Genetic Algorithm
AE '95 Selected Papers from the European conference on Artificial Evolution
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
An ant colony system for permutation flow-shop sequencing
Computers and Operations Research
Continuous interacting ant colony algorithm based on dense heterarchy
Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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In this paper, we show how an efficient ant based algorithm, called API and initially designed to perform real parameter optimization, can be adapted to the difficult problem of Hidden Markov Models training. To this aim, a transformation of the search space that preserves API's vectorial moves is introduced. Experiments are conducted with various temporal series extracted from images.