Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Anticipation in Dynamic Optimization: The Scheduling Case
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Dynamic Ant Colony Optimisation
Applied Intelligence
Generalized benchmark generation for dynamic combinatorial problems
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Hybrid techniques for dynamic optimization problems
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Population based Local Search for university course timetabling problems
Applied Intelligence
A hierarchical parallel genetic approach for the graph coloring problem
Applied Intelligence
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In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.