Title :
Adaptive Quantum-inspired Evolution Strategy
Author :
Izadinia, Hamid ; Ebadzadeh, Mohammad Mehdi
Author_Institution :
Comput. Eng. & Inf. Technol., Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
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.
Keywords :
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; Accuracy; Computers; Convergence; Logic gates; Quantum computing; Standards; Vectors; Evolution strategy; adaptive step-size; mutation operator; quantum computing;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256433
