A genetic algorithms tutorial tool for numerical function optimisation
Proceedings of the 2nd conference on Integrating technology into computer science education
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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EDOC '01 Proceedings of the 5th IEEE International Conference on Enterprise Distributed Object Computing
The Stud GA: A Mini Revolution?
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Oppositional biogeography-based optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
An educational genetic algorithms learning tool
IEEE Transactions on Education
Blended biogeography-based optimization for constrained optimization
Engineering Applications of Artificial Intelligence
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The Evolutionary Algorithm (EA) Sandbox is an Adobe® Flex®-based graphical user interface (GUI) that provides a visual demonstration of evolutionary algorithm simulations. It allows the user to select EA parameters and algorithms (such as a basic genetic algorithm, biogeography-based optimization, and opposition-based learning), run a simulation, and view the results after each generation. The EA Sandbox is meant to be a learning tool and starting point for users, giving them the ability to examine how different parameters and algorithms perform for a number of common benchmark functions. The EA Sandbox can also be easily extended to incorporate more algorithms and problem functions.