The Efficiency of Interactive Differential Evolution in Creation of Sound Contents: In Comparison with Interactive Genetic Algorithm

2012 ◽  
Author(s):  
Makoto Fukumoto ◽  
Ryota Yamamoto ◽  
Shintaro Ogawa
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Interactive Genetic Algorithm

The Efficiency of Interactive Differential Evolution in Creation of Sound Contents

2013 ◽  
Vol 1 (2) ◽  
pp. 16-27 ◽  
Author(s):  
Makoto Fukumoto ◽  
Ryota Yamamoto ◽  
Shintaro Ogawa
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Warning Sign ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Fitness Value ◽  
The Creation ◽  
The Difference

Interactive Evolutionary Computation (IEC) is known as an effective method to create media contents suited to user’s preference and objectives. As one of the methods, we have applied Differential Evolution (DE) as evolutionary algorithm in IEC. This study investigated the efficacy of Interactive Differential Evolution (IDE) in comparison with Interactive Genetic Algorithm (IGA). Two listening experiments were conducted to investigate the efficacy: experiment 1 as a creating experiment with IDE and IGA, experiment 2 as a re-evaluating experiment. Target of the creation was warning sign sounds. Eighteen subjects participated in both of the experiments. The result of the experiment 1 showed that IDE overcame IGA, and significant increase of fitness was only observed in IDE. The result of the experiment 2, higher fitness value was observed in IDE, however, the difference between the two conditions was not significant. Parts of the results showed a possibility of IDE to create media contents.

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

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