Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Applying Self-Organised Criticality to Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Optimization with extremal dynamics
Complexity - Complex Adaptive systems: Part I
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Scalability problems of simple genetic algorithms
Evolutionary Computation
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The sandpile mutation operator for genetic algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Generation of neural networks using a genetic algorithm approach
International Journal of Bio-Inspired Computation
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This paper investigates the mutation rates of a Genetic Algorithm (GA) with the sandpile mutation. This operator, which was specifically designed for non-stationary (or dynamic) optimization problems, relies on a Self-Organized Criticality system called sandpile to self-adapt the mutation intensity during the run. The behaviour of the operator depends on the state of the sandpile and on the fitness values of the population. Therefore, it has been argued that the mutation distribution may depend on to the severity and frequency of changes and on the type of stationary function that is chosen as a base-function for the dynamic problems. An experimental setup is proposed for investigating these issues. The results show that, at least under the proposed framework, a GA with the sandpile mutation self-adapts the mutation rates to the dynamics of the problem and to the characteristics of the base-function.