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 Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

scholarly journals An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1866
Author(s):  
Kei Ohnishi ◽  
Kouta Hamano ◽  
Mario Koeppen
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Search Strategy ◽  
Empirical Investigation ◽  
Optimization Problems ◽  
Machine Learning Technique ◽  
Model Based ◽  
Fitness Value ◽  
Learning Technique ◽  
The Difference

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

Selected Papers from NaBIC 2010

2011 ◽  
Vol 15 (9) ◽  
pp. 1299-1299
Author(s):  
Yusuke Nojima ◽  
Mario K?ppen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Evolutionary Computation ◽  
Machine Intelligence ◽  
Darwinian Evolution ◽  
Special Issue ◽  
Interactive Evolutionary Computation ◽  
Biologically Inspired ◽  
Fitness Value ◽  
Wide Range

The Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010) was held at the Kitakyushu International Conference Center December 15-17, 2010, in Kitakyushu, Japan. NaBIC2010 provided a forum for researchers, engineers, and students from worldwide to discuss state-of-the-art machine intelligence and to address issues related to building human-friendly machines by learning from nature. NaBIC2010 covered a wide range of studies ? from theoretical and algorithmic studies on nature and biologically inspired computing techniques to their real-world applications. Top researchers presenting papers at NaBIC2010 were invited to contribute to this special issue. Through a fair peer review process, four extended papers have been accepted ? an acceptance rate of 50%. The first paper entitled gA Study on Computational Efficiency and Plasticity in Baldwinian Learningh by Liu and Iba analyzes Baldwinian evolution efficiency by comparing it to alternatives such as standard Darwinian evolution with no learning, Lamarckian evolution, and Baldwinian evolution with different learning and plasticity evolution. The second paper entitled gExperimental Study of a Structured Differential Evolution with Mixed Strategiesh by Ishimizu and Tagawa proposes island-based DE with ring or torus networks. The authors examine the performance of the proposed DE with the effects of different strategies. The third paper entitled gMulti-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sourcesh by Ishikawa, Misawa, Kubota, Tokiwa, Horio, and Yamakawa proposes a distributed genetic algorithm in which each subpopulation searches for a solution in different decision space. Subpopulations change size based on search progress. The fourth paper entitled gAn Extended Interactive Evolutionary Computation Using Heart Rate Variability as Fitness Value for Composing Music Chord Progressh by Fukumoto, Nakashima, Ogawa, and Imai uses heart-rate variability instead of direct human evaluations in an interactive evolutionary computation framework. As guest editors of this special issue, we would like to thank the authors for their unique and interesting contributions and the reviewers for their careful checking and invaluable comments.

PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES

2005 ◽  
Vol 05 (03) ◽  
pp. 595-616 ◽  
Author(s):  
NAWWAF KHARMA ◽  
CHING Y. SUEN ◽  
PEI F. GUO
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Practical Problem ◽  
Research Effort ◽  
Fitness Function ◽  
Image Clustering ◽  
Number Of Clusters ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Hand Image

The main objective of Project PalmPrints is to develop and demonstrate a special co-evolutionary genetic algorithm (GA) that optimizes (a clustering fitness function) with respect to three quantities, (a) the dimensions of the clustering space; (b) the number of clusters; and (c) and the locations of the various clusters. This genetic algorithm is applied to the specific practical problem of hand image clustering, with success. In addition to the above, this research effort makes the following contributions: (i) a CD database of (raw and processed) right-hand images; (ii) a number of novel features designed specifically for hand image classification; (iii) an extended fitness function, which is particularly suited to a dynamic (i.e. dimensionality varying) clustering space. Despite the complexity of the multi-optimizational task, the results of this study are clear. The GA succeeded in achieving a maximum fitness value of 99.1%; while reducing the number of dimensions (features) of the space by more than half (from 84 to 41).

AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm

2017 ◽  
Vol 24 (1) ◽  
pp. 367-373 ◽  
Author(s):  
Shibo Xi ◽  
Lucas Santiago Borgna ◽  
Lirong Zheng ◽  
Yonghua Du ◽  
Tiandou Hu
Keyword(s):  
Genetic Algorithm ◽  
Synchrotron Radiation ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Open Source ◽  
Light Source ◽  
Optimization Algorithms ◽  
Beijing Synchrotron Radiation Facility ◽  
On Line ◽  
Synchrotron Light

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

Scheme of Interactive Evoking and Evolution of Creative Ideas Based on Interactive Evolutionary Computation

2011 ◽  
Vol 48-49 ◽  
pp. 1006-1009
Author(s):  
Guo Sheng Hao ◽  
Xiang Jun Zhao ◽  
Jia Wei Wu ◽  
Yong Qing Huang
Keyword(s):  
Evolutionary Computation ◽  
Interactive Evolutionary Computation ◽  
New Ideas ◽  
The Creation

Creative ideas are important not only for an enterprise but also for a country. The scheme for computer to produce creative ideas is given. There are two parts in the scheme. The first part is for computer to realize the creation skills, which will bring thousands or millions of candidate ideas. The second part is to optimize the candidate ideas with interactive evolutionary computation (IEC). When the sentences that IEC optimized are submitted to the user, his/her thought would be invoked, and new ideas would come to his/her brain. This scheme supports a new way for computer to help user produce creative ideas.

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