System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Scaling Up Evolutionary Programming Algorithms
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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It has been found that both evolutionary programming (EP) and fast EP (FEP) could get stuck in local optima on some test functions. Although a number of methods have been developed to solve this problem, nearly all have focused on how to adjust search step sizes. This paper shows that it is not enough to change the step sizes alone. Besides step control, the shape of search space should be changed so that the search could be driven to other unexplored regions without getting stuck in the local optima. A two-level FEP with deletion is proposed in this paper to make FEP robust on finding better solutions in function optimisation. A coarse-grained search in the upper level could lead FEP to generate a diverse population, while a fine-grained search in the lower level would help FEP quickly find a local optimum in a region. After FEP could not make any progress after falling in a local optimum, deletion would be applied to change the search space so that FEP could start a new fine-grained search from the points generated by the coarse-grained search.