Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
An analysis of the XOR dynamic problem generator based on the dynamical system
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A comparison of GE and TAGE in dynamic environments
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Examining grammars and grammatical evolution in dynamic environments
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Analysing fitness landscape changes in evolutionary robots
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A differential evolution approach for solving constrained min-max optimization problems
Expert Systems with Applications: An International Journal
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
ACO beats EA on a dynamic pseudo-boolean function
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Runtime analysis of ant colony optimization on dynamic shortest path problems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Challenges and opportunities in dynamic optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolutionary computation for dynamic optimization problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Boolean functions, given a simple but well-defined dynamic framework. We demonstrate some counter-intuitive scenarios that allow us to gain a better understanding of how the dynamics of a function may affect the runtime of an algorithm. In particular, we present the function Magnitude, where the time it takes for the (1+1) EAdyn to relocate the global optimum is less than n2log n (i.e., efficient) with overwhelming probability if the magnitude of change is large. For small changes of magnitude, on the other hand, the expected time to relocate the global optimum is eΩ(n) (i.e., highly inefficient). Similarly, the expected runtime of the (1+1) EAdyn on the function Balance is O(n2) (efficient) for a high frequencies of change and nΩ(√n) (highly inefficient) for low frequencies of change. These results contribute towards a better understanding of dynamic optimisation problems in general and show how traditional analytical methods may be applied in the dynamic case.