New computer methods for global optimization
New computer methods for global optimization
The annealing evolution algorithm as function optimizer
Parallel Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Generation of optimal trajectories for ascending and descending a stair of a humanoid based on uDEAS
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.