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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms in time-dependent environments
Theoretical aspects of evolutionary computing
ACM Transactions on Mathematical Software (TOMS)
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Performance Measures for Dynamic Multi-Objective Optimization
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Memetic algorithm for dynamic bi-objective optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Natural Computing: an international journal
Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Multi-swarm co-evolutionary paradigm for dynamic multi-objective optimisation problems
International Journal of Intelligent Information and Database Systems
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Simplex model based evolutionary algorithm for dynamic multi-objective optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A predictive evolutionary algorithm for dynamic constrained inverse kinematics problems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Using genetic algorithms for navigation planning in dynamic environments
Applied Computational Intelligence and Soft Computing
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
International Journal of Metaheuristics
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This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combine a forecasting technique with an evolutionary algorithm. The location, in variable space, of the optimal solution (or of the Pareto optimal set in multi-objective problems) is estimated using a forecasting method. Then, using this forecast, an anticipatory group of individuals is placed on and near the estimated location of the next optimum. This prediction set is used to seed the population when a change in the objective landscape arrives, aiming at a faster convergence to the new global optimum. The forecasting model is created using the sequence of prior optimum locations, from which an estimate for the next location is extrapolated. Conceptually this approach encompasses advantages of memory methods by making use of information available from previous time steps. Combined with a convergence/diversity balance mechanism it creates a robust algorithm for dynamic optimization. This strategy can be applied to single objective and multi-objective problems, however in this work it is tested on multi-objective problems. Initial results indicate that the approach improves algorithm performance, especially in problems where the frequency of objective change is high.