An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
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Theoretical analysis of the properties of evolutionary algorithms is very important to understand their search behaviors and to develop more efficient algorithms. This article investigates the convergence properties of a canonical Differential Evolution (DE) algorithm with DE/rand/1 type mutation and binomial crossover. For simplicity and to provide an insight into the heuristics of the algorithm, the analysis has been done by assuming a single-dimensional fitness function f(x) . The analysis is independent of the nature of the objective function as long as it remains real-valued and possesses an unique global optimum (it may have multiple local optima as well).