TY  - BOOK
AU  - Nissrine Mohammad Alarabi Albarrak
AU  - Hamiden Abdelwahed Khalifa , 
AU  - Hegazy Zaher , 
TI  - An evolutionary algorithm for solving optimization problems / 
PY  - 2019///
CY  - Cairo : 
PB  - Nissrine Mohammad Alarabi Albarrak , 
KW  - Crossover rate
KW  - Differential evolution
KW  - Evolutionary algorithms
N1  - Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research; Issued also as CD
N2  - Many optimization problems have characteristics that make them difficult to solve from traditional algorithms. Evolutionary Algorithms can deal with complex optimization problems better than traditional optimization techniques. The key aspect distinguishing an evolutionary search algorithm from such traditional algorithms is that it is population-based. Differential Evolution is one branch of evolutionary algorithms, is capable of addressing a wide set of such optimization problem in a relatively uniform and conceptually simple manner. This Thesis proposes an alternative differential evolution algorithm for solving unconstrained optimization problems. The performance of the given algorithm is measured by the result of 15 benchmarking problems the obtained results are competent in both accuracy and CPU time. The results obtained using the proposed algorithm are more accurate and use less number of function{u2019}s evaluations compared with several algorithms. This Thesis contents of four chapters Chapter 1 describes optimization definition, classification of optimization problem and history of differential evolution algorithm. Chapter 2 describes the different type of optimization techniques, evolutionary algorithms and meta-heuristic techniques. Chapter 3 describes the definition of differential evolution algorithm, the main stages of differential evolution algorithm different application of differential evolution and some type of differential evolution algorithms
ER  -