Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming

Park, Ho-Hyun; Grings, Alexandre; Santos, Maribel Yasmina; Soares, Alexsandro Santos

doi:10.1016/j.amc.2007.12.002

Cited by 13 publications

(21 citation statements)

References 6 publications

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“…It is noteworthy that most researchers had to solve the problem as a problem of a single goal , where the only goal is to reduce all constraints (Abramson and Abela 1992;Blum; 2002; Lima; 2001; Piola; 1994), but there are many options to define goals such as access to a timetable orderly and correctly, and a number of successive lectures to the instructor and others that make the problem of scheduling multiple objective problems, there are a few researchers who define the problem as a problem of multiple targets. Carrasc and Pat (2001) used the dual objective model to the problem of scheduling courses in school schedules to reduce soft constraints for the teacher and the class, Filho and Lorena (2001) they also define the problem as a dual goal, Dicev (2004) identified the problem as a dual goal, but the definition of different goals to reduce the gap between lecture time and the date of the completion of daily lectures, so that priority for early lectures given in one of the goals, and priority late for lectures given at the other goal [7].…”

Section: Parallel Algorithmmentioning

confidence: 99%

Parallel and Distributed Genetic Algorithm with Multiple-Objectives to Improve and Develop of Evolutionary Algorithm

Abuzanouneh¹

2016

ijacsa

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Abstract-In this paper, we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it's mean that necessary operations to solve the problem will increases exponentially and directly proportional to the size of the problem, The construction of timetable is most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems ,it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distributed models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.

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Section: Parallel Algorithmmentioning

confidence: 99%

Parallel and Distributed Genetic Algorithm with Multiple-Objectives to Improve and Develop of Evolutionary Algorithm

Abuzanouneh¹

2016

ijacsa

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“…The PGEP-O system [3] is another example of a parallel GEP algorithm which used two island groups. The first island group was a standard Island Model implementation, in which a single population of individuals was evolved on each island.…”

Section: Distributed Evolutionmentioning

confidence: 99%

“…Park et al introduced a parallel system, PGEP-O [3], which attempted to dynamically tune specific parameters of the GEP algorithm. In the work, the individuals were constrained by the genome restrictions of canonical GEP; that is, only identically structured individuals were able to interact and exist within a single population or island.…”

Section: Crossbreeding and Speciationmentioning

confidence: 99%

“…The EGIPSYS algorithm [14] permitted different-sized chromosomes within a population, which other systems, such as canonical GEP, AdaGep [19], and PGEP-O [3], do not support. However, unlike our proposed methodology, all individuals in an EGIPSYS population were required to have the same number of genes.…”

Section: Crossbreeding and Speciationmentioning

confidence: 99%

“…Additionally, the symbolic regression results were compared to the adaptive distributed GEP algorithm developed by Park et al [3]. The results obtained using an application developed during the course of this work, known as Syrah, and the results were validated using the K-Fold method with 10 folds.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Browne

Santos

2010

Applied Computational Intelligence and Soft Computing

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Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

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From Metaheuristics to Automatic Programming

Elleuch

Jarboui

Siarry

2022

Computational Intelligence Methods and Applications

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Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming

Cited by 13 publications

References 6 publications

Parallel and Distributed Genetic Algorithm with Multiple-Objectives to Improve and Develop of Evolutionary Algorithm

Parallel and Distributed Genetic Algorithm with Multiple-Objectives to Improve and Develop of Evolutionary Algorithm

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

From Metaheuristics to Automatic Programming

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