Abstract | ||
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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. |
Year | DOI | Venue |
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2012 | 10.1109/CEC.2012.6256433 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
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 | Convergence (routing),Mathematical optimization,Adaptive mutation,Computer science,Evolutionary computation,Quantum computer,Evolution strategy,Gaussian process,CMA-ES,Artificial intelligence,Optimization problem,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-1508-1 | 0 | 0.34 |
References | Authors | |
5 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hamid Izadinia | 1 | 164 | 11.16 |
Mohamad M. Ebadzadeh | 2 | 19 | 2.38 |