The Exponential Crossover in L-shade Algorithm
نویسنده
چکیده
Differential evolution is popular and efficient algorithm for global optimization. L-SHADE algorithm is one of the most successful adaptive versions of the algorithm. It uses only binomial crossover. We study employing the exponential crossover in the algorithm. Our tests are carried out on CEC2015 benchmark set for learning-based optimization competition. According to our results, the employing of the exponential crossover together with binomial one into L-SHADE algorithm is beneficial.
منابع مشابه
Evolución diferencial con memoria de parámetros para la optimización de mecanismos de cuatro barras
L-SHADE is an algorithm based on differential evolution wich automatically adapts the parameters related to mutation (F ) and crossover (CR) using a parameter memory. L-SHADE also controls the population size (NP ) with a linear function. In this work, a constrainthandling technique is added to L-SHADE to optimize three four-bar
متن کاملGenetic algorithm for Echo cancelling
In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers; heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and diff...
متن کاملReevaluating Exponential Crossover in Differential Evolution
Exponential crossover in Differential Evolution (DE), which is similar to 1-point crossover in genetic algorithms, continues to be used today as a default crossover operator for DE. We demonstrate that exponential crossover exploits an unnatural feature of some widely used synthetic benchmarks such as the Rosenbrock function – dependencies between adjacent variables. We show that for standard D...
متن کاملDesign and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Real Royal Road Functions — Where Crossover Provably Is Essential Real Royal Road Functions — Where Crossover Provably Is Essential *
Mutation and crossover are the main search operators of different variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some explicitly defined fitness functions fn : {0, 1} n → R that a genetic algorithm with crossover (but without idealization) can optimize fn in expected polynomial time while all evolution strategies...
متن کاملUniversity of Dortmund Reihe Computational Intelligence
Mutation and crossover are the main search operators of different variants of evolutionary algorithms. Despite the many discussions on the importance of crossover nobody has proved rigorously for some explicitly defined fitness functions fn : {0, 1} n → R that a genetic algorithm with crossover (but without idealization) can optimize fn in expected polynomial time while all evolution strategies...
متن کامل