Research on Methodology of Evolutionary Computing

2012 ◽  
Vol 490-495 ◽  
pp. 524-528
Author(s):  
Jun Fei Zhuo ◽  
Xing He Wu ◽  
Guan Zhao Wu ◽  
Min Yao
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Computational Intelligence ◽  
Gene Expression Programming ◽  
Evolutionary Computing ◽  
Estimation Of Distribution Algorithms ◽  
Swarm Optimization ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

Evolutionary computing is one of the important branches in computational intelligence. This paper mainly introduces four new branches of the evolutionary computation, i.e. Gene Expression Programming (GEP), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Estimation of Distribution Algorithms (EDA).

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2021 ◽  
pp. 1-34
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

Algorithm of Coal Mine Rescue Robot Model Based on PSO and GEP

2013 ◽  
Vol 416-417 ◽  
pp. 739-742
Author(s):  
Xue Chen Wang ◽  
Xiao Guang Yue
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Coal Mine ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Programming Algorithm ◽  
Swarm Optimization ◽  
Rescue Robot ◽  
Model Based ◽  
Mine Rescue

In order to study a mine rescue robot model, gene expression programming algorithm is studied. The gene expression programming Algorithm can simulate many scientific models, and has been successfully applied in many aspects. Particle swarm optimization algorithm is discussed. Each member of the particle swarm optimization group can study its own experience and other members' experience to continuously change their search mode. Finally, a coal mine rescue robot model based on the gene expression programming and particle swarm optimization is put forward.

UNSUPERVISED LEARNING OF BAYESIAN NETWORKS VIA ESTIMATION OF DISTRIBUTION ALGORITHMS: AN APPLICATION TO GENE EXPRESSION DATA CLUSTERING

2004 ◽  
Vol 12 (supp01) ◽  
pp. 63-82 ◽  
Author(s):  
J. M. PEÑA ◽  
J. A. LOZANO ◽  
P. LARRAÑAGA
Keyword(s):  
Gene Expression ◽  
Unsupervised Learning ◽  
Gene Expression Data ◽  
Data Clustering ◽  
Real Data ◽  
Estimation Of Distribution Algorithms ◽  
Expression Data ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Gene Expression Data Clustering

This paper proposes using estimation of distribution algorithms for unsupervised learning of Bayesian networks, directly as well as within the framework of the Bayesian structural EM algorithm. Both approaches are empirically evaluated in synthetic and real data. Specifically, the evaluation in real data consists in the application of this paper's proposals to gene expression data clustering, i.e., the identification of clusters of genes with similar expression profiles across samples, for the leukemia database. The validation of the clusters of genes that are identified suggests that these may be biologically meaningful.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2017 ◽  
pp. 325-358
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

Shear strength prediction of short circular reinforced-concrete columns using soft computing methods

2020 ◽  
Vol 23 (14) ◽  
pp. 3048-3061
Author(s):  
Hesam Ketabdari ◽  
Farzad Karimi ◽  
Mahsa Rasouli
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Shear Strength ◽  
Particle Swarm Optimization ◽  
Reinforced Concrete ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Concrete Columns ◽  
Swarm Optimization ◽  
Reinforced Concrete Columns

In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

scholarly journals Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

2021 ◽  
Author(s):  
Elmira Ghoulbeigi
Keyword(s):  
Probability Distribution ◽  
Gene Expression Programming ◽  
Solution Space ◽  
Test Problems ◽  
Estimation Of Distribution Algorithms ◽  
Indirect Estimation ◽  
Lawn Mower ◽  
Estimation Of Distribution ◽  
N Gram ◽  
Distribution Algorithms

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.

scholarly journals Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

2021 ◽  
Author(s):  
Elmira Ghoulbeigi
Keyword(s):  
Probability Distribution ◽  
Gene Expression Programming ◽  
Solution Space ◽  
Test Problems ◽  
Estimation Of Distribution Algorithms ◽  
Indirect Estimation ◽  
Lawn Mower ◽  
Estimation Of Distribution ◽  
N Gram ◽  
Distribution Algorithms

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.

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