Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Investigation of a Cellular Genetic Algorithm that Mimics Landscape Ecology
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Swarm: An Object Oriented Simulation Platform Applied to Markets and Organizations
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
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Parallel Genetic Algorithms (PGA) have been implemented in the past largely on parallel computers, and more recently on serial PCs. PGAs have been used successfully in solving many difficult optimization tasks. To gain further insight into the state and progress of the algorithm, we often need to extract useful information from the large amount of data generated from a PGA run, but this can be a difficult task. Many of the current PGA implementations often have no capability of visualizing an evolving GA population dynamically during execution time. In this paper, we describe an implementation of a fine-grained parallel GA using Swarm, a multi-agent simulation tool originally developed at the Santa Fe institute. The PGA model developed is capable of visualizing dynamically the performance of an evolving GA population with plotted graphs on model parameter values in real time. This implementation also allows modification of some model parameter values during an optimization run, therefore offers advantages over many existing PGA implementations. We demonstrate the usefulness of the visualization techniques used in this PGA implementation using two optimization examples.