Add to ORKGAbstract—Several adaptive variants of differential evolution are described and compared in two sets of benchmark problems. The influence of exponential crossover on efficiency of the search is studied. The use of both types of crossover together makes the algorithms more robust. Such algorithms are convenient for the real-world problems, where we need an adaptive algorithm applicable without time-wasting parameter tuning. I
Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have...
One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operator...
Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). It has demonstrat...
Abstract. Exponential crossover in Differential Evolution (DE), which is similar to 1-point crossove...
The performance of Differential Evolution (DE) algorithm is significantly affected by its parameter ...
Differential Evolution is an evolutionary algorithm designed for global optimization. Its main asset...
Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA) for global numerica...
The performance of most metaheuristic algorithms depends on parameters whose settings essentially se...
The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-base...
194 p.This thesis investigates a relatively new technique in the global optimization field, namely D...
Designing an efficient optimization method which also has a simple structure is generally required b...
A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr 2 [0...
Real life problems which deal with time varying landscape dynamics often pose serious challenge to t...
Although the Differential Evolution (DE) algorithm is a powerful and commonly used stochastic evolut...
The performance of most metaheuristic algorithms depends on parameters whose settings essentially se...
Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have...
One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operator...
Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). It has demonstrat...
Abstract. Exponential crossover in Differential Evolution (DE), which is similar to 1-point crossove...
The performance of Differential Evolution (DE) algorithm is significantly affected by its parameter ...
Differential Evolution is an evolutionary algorithm designed for global optimization. Its main asset...
Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA) for global numerica...
The performance of most metaheuristic algorithms depends on parameters whose settings essentially se...
The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-base...
194 p.This thesis investigates a relatively new technique in the global optimization field, namely D...
Designing an efficient optimization method which also has a simple structure is generally required b...
A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr 2 [0...
Real life problems which deal with time varying landscape dynamics often pose serious challenge to t...
Although the Differential Evolution (DE) algorithm is a powerful and commonly used stochastic evolut...
The performance of most metaheuristic algorithms depends on parameters whose settings essentially se...
Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have...
One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operator...
Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). It has demonstrat...