Adaptive Quantum-inspired Evolution Strategy
IEEE Congress on Evolutionary Computation（2012）
摘要
Standard Evolution Strategy (ES) produces the next generation via the Gaussian mutation that is not directed toward the optimum. Additionally, self-adaptation mechanism is used in the standard ES to adapt mutation step-size. This paper presents a new evolution strategy which is called Quantum-inspired Evolution Strategy (QES). QES applies a new learning mechanism whereby the information of the mutants is used as a feedback to adapt the mutation direction and step-size simultaneously. To demonstrate the effectiveness of the proposed method, several experiments on a set of numerical optimization problems are carried out and the results are compared with the standard ES and Covariance Matrix Adaptation ES (CMA-ES) which is the state-of-the-art method for adaptive mutation. The results reveal that QES is superior to standard ES and CMA-ES in terms of convergence speed and accuracy.
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关键词
Gaussian processes,convergence,covariance matrices,evolutionary computation,learning (artificial intelligence),optimisation,quantum computing,CMA-ES,Gaussian mutation,QES,adaptive mutation,adaptive quantum-inspired evolution strategy,convergence speed,covariance matrix adaptation ES,learning mechanism,mutant information,mutation direction,mutation step-size adaptation,numerical optimization problem,self-adaptation mechanism,standard evolution strategy,Evolution strategy,adaptive step-size,mutation operator,quantum computing
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