scholarly journals EvoPER -- An R package for applying evolutionary computation methods in the parameter estimation of individual-based models implemented in Repast

2016 ◽  
Author(s):  
Antonio Prestes García ◽  
Alfonso Rodríguez-Patón
Keyword(s):  
Parameter Estimation ◽  
Evolutionary Computation ◽  
Combinatorial Problem ◽  
Search Space ◽  
R Package ◽  
Current Version ◽  
Computation Techniques ◽  
Acceptable Range ◽  
Individual Based Models ◽  
Input Parameters

Individual-based models are complex and they normally have an elevated number of input parameters which must be tuned in order to reproduce the experimental or observed data as accurately as possible. Hence one of the weakest points of such kind of models is the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Therefore, several parameter combinations must be checked to find an acceptable set of input factors minimizing the deviations of simulated and observed data. In practice, most of times, is computationally unfeasible to traverse the complete search space to check all parameter combination in order to find the best of them. That is precisely the kind of combinatorial problem suitable for evolutionary computation techniques. In this work we present the EvoPER, an R package for simplifying the parameter estimation using evolutionary computation techniques. The current version of EvoPER includes implementations of PSO, SA and ACO algorithms for parameter estimation of Repast models.

scholarly journals EvoPER -- An R package for applying evolutionary computation methods in the parameter estimation of individual-based models implemented in Repast

2016 ◽  
Author(s):  
Antonio Prestes García ◽  
Alfonso Rodríguez-Patón
Keyword(s):  
Parameter Estimation ◽  
Evolutionary Computation ◽  
Combinatorial Problem ◽  
Search Space ◽  
R Package ◽  
Current Version ◽  
Computation Techniques ◽  
Acceptable Range ◽  
Individual Based Models ◽  
Input Parameters

Individual-based models are complex and they normally have an elevated number of input parameters which must be tuned in order to reproduce the experimental or observed data as accurately as possible. Hence one of the weakest points of such kind of models is the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Therefore, several parameter combinations must be checked to find an acceptable set of input factors minimizing the deviations of simulated and observed data. In practice, most of times, is computationally unfeasible to traverse the complete search space to check all parameter combination in order to find the best of them. That is precisely the kind of combinatorial problem suitable for evolutionary computation techniques. In this work we present the EvoPER, an R package for simplifying the parameter estimation using evolutionary computation techniques. The current version of EvoPER includes implementations of PSO, SA and ACO algorithms for parameter estimation of Repast models.

Crossover Invariant Subsets of the Search Space for Evolutionary Algorithms

2004 ◽  
Vol 12 (1) ◽  
pp. 19-46 ◽  
Author(s):  
Boris Mitavskiy
Keyword(s):  
Theoretical Analysis ◽  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Search Space ◽  
Current Paper ◽  
Mathematical Framework ◽  
Mathematical Apparatus ◽  
Computation Techniques ◽  
The Family ◽  
The Relationship

This paper addresses the relationship between schemata and crossover operators. In Appendix A a general mathematical framework is developed which reveals an interesting correspondence between the families of reproduction transformations and the corresponding collections of invariant subsets of the search space. On the basis of this mathematical apparatus it is proved that the family of masked crossovers is, for all practical purposes, the largest family of transformations whose corresponding collection of invariant subsets is the family of Antonisse's schemata. In the process, a number of other interesting facts are shown. It is proved that the full dynastic span of a given subset of the search space under either one of the traditional families of crossover transformations (one-point crossovers or masked crossovers) is obtained after [log2n] iterations where n is the dimension of the search space. The generalized notion of invariance introduced in the current paper unifies Radcliffe's notions of firespectfl and figene transmissionfl. Besides providing basic tools for the theoretical analysis carried out in the current paper, the general facts established in Appendix A provide a way to extend Radcliffe's notion of figenetic representation functionfl to compare various evolutionary computation techniques via their representation.

A Comparative Study of Evolutionary Computation Techniques for Solar Cells Parameter Estimation

2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Omar Avalos ◽  
Erik Valdemar Cuevas Jimenez ◽  
Arturo Valdivia-González ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Solar Cells ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Computation Techniques

mvClaim : an R package for multivariate general insurance claims severity modelling

2021 ◽  
pp. 1-17
Author(s):  
Sen Hu ◽  
T. Brendan Murphy ◽  
Adrian O’Hagan
Keyword(s):  
R Package ◽  
Finite Mixture ◽  
Current Version ◽  
Mixture Of Experts ◽  
Insurance Claim ◽  
General Insurance ◽  
Gamma Distributions ◽  
Approach Theory ◽  
Modelling Approach ◽  
Model Family

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

Users' Fuzzy Cognition Knowledge Learning in Interactive Evolutionary Computation and its Application

2011 ◽  
Vol 204-210 ◽  
pp. 245-250
Author(s):  
Guo Sheng Hao ◽  
Xiang Jun Zhao ◽  
Yong Qing Huang
Keyword(s):  
Evolutionary Computation ◽  
Search Space ◽  
Interactive Evolutionary Computation ◽  
Sense Unit ◽  
Knowledge Learning

user in interactive evolutionary computation (IEC) has the characteristic of fuzzy cognition. Based on this, a method to learn users’ fuzzy cognition knowledge is given. The method includes the fuzzy expression of the basic elements of IEC such as search space, population, gene sense unit and so on. Then a method to increase the performance of IEC based on the knowledge of users’ fuzzy cognition is given. The above results enrich the researches of IEC users' cognition.

Joint DNA-based Disaster Victim Identification

2021 ◽  
Author(s):  
Magnus Dehli Vigeland ◽  
Thore Egeland
Keyword(s):  
A Priori ◽  
Search Space ◽  
R Package ◽  
Individual Identification ◽  
Disaster Victim Identification ◽  
Data Set ◽  
Victim Identification ◽  
Joint Identification ◽  
Available Information ◽  
User Friendly

Abstract We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

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