A stochastic calculus model of continuous trading: optimal portfolios
Mathematics of Operations Research
A parallel genetic algorithm for the set partitioning problem
A parallel genetic algorithm for the set partitioning problem
Practical genetic algorithms
Dynamic optimization for reachability problems
Journal of Optimization Theory and Applications
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Knowledge and population swarms in cultural algorithms for dynamic environments
Knowledge and population swarms in cultural algorithms for dynamic environments
Mathematics and Computers in Simulation
A novel multi-population cultural algorithm adopting knowledge migration
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Recent progress in natural computation and knowledge discovery
Evolved election forecasts: using genetic algorithms in improving election forecast results
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
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The optimisation of dynamic problems is both widespread and difficult. When conducting dynamic optimisation, a balance between re-initialisation and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimise a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make optimisations more efficient. But they adapt slowly to changing environments. We thus suggest a confluence of these approaches: revolutionary algorithms. These algorithms seek to extend the evolutionary and cultural aspects of the former two approaches with a notion of the political. By modelling how belief systems are changed by means of revolution, these algorithms provide a framework to model and optimise dynamic problems in an efficient fashion. The superiority of revolutionary algorithms over cultural and purely genetic algorithms is demonstrated in the solving of a standard dynamic facility location problem.