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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Exploratory analysis of an on-line evolutionary algorithm in simulated robots
TL;DR: This paper presents an evolutionary algorithm belonging to the first category of on-line evolution for developing robot controllers, and uses the Bonesa parameter tuning method to explore its parameter space, showing that it seems preferable to try many alternative solutions and spend little effort on refining possibly faulty assessments.
Book ChapterDOI
Experimental Results in Function Optimization with EDAs in Continuous Domain
TL;DR: In this paper, experimental results of applying continuous Estimation of Distribution Algorithms (EDA) to some well known optimization problems were presented. But their performance was compared to such of Evolution Strategies (Schwefel, 1995).
Journal ArticleDOI
System identification using evolutionary Markov chain Monte Carlo
Byoung-Tak Zhang,Dong-Yeon Cho +1 more
TL;DR: Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.
Journal ArticleDOI
Convergence of nomadic genetic algorithm on benchmark mathematical functions
S. Siva Sathya,M. V. Radhika +1 more
TL;DR: To compare its performance with standard GA (SGA), the prominent mathematical functions used in optimization are used and the results proved that NGA outperforms SGA in terms of convergence speed and better optimized values.
Journal Article
Assessing model calibration adequacy via global optimisation
J.G. Ndiritu,Trevor Daniell +1 more
TL;DR: In this paper, an assessment of the application of varying levels of optimisation on model simulation performance and parameter identification was done using the genetic algorithm (GA) and a 10-parameter version of the MODHYDROLOG rainfall-runoff model, four levels of optimization were obtained through the use of two GA formulations, the traditional and an improved GA, and by varying the optimisation parameters with each formulation.