This research paper presents a new approach to population size reduction in Success-History based Adaptive Differential Evolution (SHADE). The current L-SHADE algorithm uses fitness function value to select individuals which will be deleted from the current population. Algorithm variant proposed in this paper (Net L-SHADE) is using the information from evolutionary process to construct a network of individuals and the ones which would be deleted are selected based on their degree of centrality. The proposed technique is compared to state-of-art L-SHADE on CEC2015 benchmark set and the results are reported
In this paper, a comparative study of seven variants of the Success-History based Adaptive Different...
This paper studies the efficiency of a recently defined population-based direct global optimization ...
In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evo...
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adapt...
This research paper presents an analysis of the population activity in Differential Evolution algori...
This paper provides an analysis of the population clustering in a novel Success-History based Adapti...
In this preliminary study, the dynamic of continuous optimization algorithm Success-History based Ad...
This paper deals with the Success-History based Adaptive Differential Evolution with Linear decrease...
This research paper analyses an external archive of inferior solutions used in Success-History based...
This paper provides an analysis of the population clustering in a novel Success-History based Adapti...
Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-c...
This paper discusses the effect of distance based parameter adaptation on the population diversity o...
This preliminary study presents a hybridization of two research fields – evolutionary algorithms and...
This paper presents a novel approach to visualizing Evolutionary Algorithm (EA) dynamic in complex n...
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adapt...
In this paper, a comparative study of seven variants of the Success-History based Adaptive Different...
This paper studies the efficiency of a recently defined population-based direct global optimization ...
In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evo...
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adapt...
This research paper presents an analysis of the population activity in Differential Evolution algori...
This paper provides an analysis of the population clustering in a novel Success-History based Adapti...
In this preliminary study, the dynamic of continuous optimization algorithm Success-History based Ad...
This paper deals with the Success-History based Adaptive Differential Evolution with Linear decrease...
This research paper analyses an external archive of inferior solutions used in Success-History based...
This paper provides an analysis of the population clustering in a novel Success-History based Adapti...
Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-c...
This paper discusses the effect of distance based parameter adaptation on the population diversity o...
This preliminary study presents a hybridization of two research fields – evolutionary algorithms and...
This paper presents a novel approach to visualizing Evolutionary Algorithm (EA) dynamic in complex n...
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adapt...
In this paper, a comparative study of seven variants of the Success-History based Adaptive Different...
This paper studies the efficiency of a recently defined population-based direct global optimization ...
In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evo...