Compact Differential Evolution

作     者：Mininno, Ernesto Neri, Ferrante Cupertino, Francesco Naso, David 

作者机构：Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40700 Finland Acad Finland FI-00501 Helsinki Finland Tech Univ Bari Dept Elect & Elect Engn I-70100 Bari Italy Polytech Inst Bari Dept Elect & Elect Engn I-70126 Bari Italy 

出 版 物：《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》 (IEEE Trans Evol Comput)

年 卷 期：2011年第15卷第1期

页      面：32-54页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Academy of Finland, Akatemiatutkija Algorithmic Design Issues in Memetic Computing Tekes (the Finnish Funding Agency for Technology and Innovation) [40214/08] Academy of Finland (AKA) Funding Source: Academy of Finland (AKA) 

主　　题：Adaptive systems compact genetic algorithms differential evolution (DE) estimation distribution algorithms 

摘      要：This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neural-network-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental