Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Are Evolutionary Algorithms Improved by Large Mutations?
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
EURASIP Journal on Applied Signal Processing
Population size reduction for the differential evolution algorithm
Applied Intelligence
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Heuristic search strategy of evolutionary programming
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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Evolution Strategies (ES) are an approach to numerical optimization that shows good optimization performance. However, it is found through our computer simulations that the performance changes with the lower bound of strategy parameters, although it has been overlooked in the ES community. We demonstrate that a population cannot practically move to other better points, because strategy parameters attain minute values at an early stage, when too small a lower bound is adopted. This difficulty is called the lower bound problem in this paper. In order to improve the “self-adaptive” property of strategy parameters, a new extended ES called RES is proposed. RES has redundant neutral strategy parameters and adopts new mutation mechanisms in order to utilize selectively neutral mutations so as to improve the adaptability of strategy parameters. Computer simulations of the proposed approach are conducted using several test functions.