Randomized algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Introduction to Algorithms
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
An Analysis Of The Role Of Offspring Population Size In EAs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On the design of problem-specific evolutionary algorithms
Advances in evolutionary computing
Analysis of local operators applied to discrete tracking problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimum tracking with evolution strategies
Evolutionary Computation
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
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Evolutionary algorithms are often applied for solving optimization problems that are too complex or different from classical problems so that the application of classical methods is difficult. One example are dynamic problems that change with time. An important class of dynamic problems is the class of tracking problems where an algorithm has to find an approximately optimal solution and insure an almost constant quality in spite of the changing problem. For the application of evolutionary algorithms to static optimization problems, the distribution of the optimization time and most often its expected value are most important. Adopting this perspective a simple tracking problem in the lattice is considered and the performance of a mutation-based evolutionary algorithm is evaluated. For the static case, asymptotically tight upper and lower bounds are proven. These results are applied to derive results on the tracking performance for different rates of change.