Cooperative coevolution and univariate estimation of distribution algorithms

Vo, Christopher; Panait, Liviu; Luke, Sean

doi:10.1145/1527125.1527144

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the CCEA strategy looks on a set or subset of individuals of the population as a final solution. This means that every individual is a part of the solution, and all individuals collaborating to build the final solution [19].…”

Section: Evolutionary Sound Synthesismentioning

confidence: 99%

Automatic sound synthesis using the fly algorithm

et al. 2020

View full text Add to dashboard Cite

Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies' representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language.

show abstract

Section: Evolutionary Sound Synthesismentioning

confidence: 99%

Automatic sound synthesis using the fly algorithm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Recent theoretical connection has been established between univariate EDAs and CCEAs [10] which opens up the possibility of using ideas from cooperative coevolution to improve univariate EDAs. The CCEA community has long understood that these (essentially identical) performance difficulties are due to certain game-theoretic anomalies arising from the multiple optimization procedures occurring in parallel along each margin [11].…”

Section: Related Workmentioning

confidence: 99%

Do multiple trials help Univariate methods?

Rothman

Luke

Sullivan

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

Self Cite

View full text Add to dashboard Cite

Cooperative Coevolutionary Algorithms (CCEAs) and Univariate Estimation of Distribution Algorithms (Univariate EDAs) are closely related algorithms in that both update marginal distributions/populations, and test samples of those distributions/populations by grouping them with collaborators drawn from elsewhere to form a complete solution. Thus the quality of these samples is context-sensitive and the algorithms assume low linkage among their variables. This results in well-known difficulties with these methods. While EDAs have commonly overcome these difficulties by examining multivariate linkage, CCEAs have instead examined basing the fitness of each marginal sample on the maximum of several trials. In this study we examine whether multiple-trial CCEA approach is really effective for difficult problems and large numbers of subpopulations; and whether this approach can be used to improve Univariate EDAs as well.

show abstract