Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Macroevolutionary algorithms: a new optimization method on fitnesslandscapes
IEEE Transactions on Evolutionary Computation
A Religion-Based Spatial Model for Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Self-adaptive Operator Scheduling Using the Religion-Based EA
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A study on the mutation rates of a genetic algorithm interacting with a sandpile
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
The sandpile mutation operator for genetic algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Complex systems are typically composed of a large number of locally interacting components that operate at a critical state between chaos and order, which is known as self-organised criticality. A common feature of this state is the exponential (power law) relationship between the frequency of an event and the size of its impact, such as the event of an earthquake and its strength on the Richter scale. Most state transitions in a component of a complex system only affect its neighbourhood, but once in a while entire avalanches of propagating state transitions can lead to a major reconfiguration of the system. In evolution, this system behaviour has been identified in species extinction on an evolutionary time-scale, where avalanches correspond to mass extinction. In this paper, we applied the concept of self-organised criticality (SOC) to control mutation on the individual level and extinction on the population level in the context of evolutionary algorithms (EA). Our results show that the SOC EAs clearly outperform standard EAs and a previously introduced mass extinction model. Interestingly, the great performance of our SOC EAs is based on a very simple modification of standard EAs and implies almost no additional computational costs.