Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Adapting Self-Adaptive Parameters in Evolutionary Algorithms
Applied Intelligence
Particle swarm optimization with simulated annealing for TSP
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Simulated annealing Based-GA using injective contrast functions for BSS
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Composite particle optimization with hyper-reflection scheme in dynamic environments
Applied Soft Computing
A multiple local search algorithm for continuous dynamic optimization
Journal of Heuristics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.