A Comparison of Differential Evolution and Generalized Generation Gap Model

Rönkkönen, Jani; Kukkonen, Saku; Lampinen, Jouko

doi:10.20965/jaciii.2005.p0549

Cited by 5 publications

(3 citation statements)

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“…As a result, this procedure reduces the computational costs of the metaheuristic and improves its efficiency. Readers interested in GDE3 should refer to the texts by [ 30 , 31 ].…”

Section: Generalized Differential Evolution Metaheuristicmentioning

confidence: 99%

Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Adekanmbi

Green

2015

The Scientific World Journal

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Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

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“…As a result, this procedure reduces the computational costs of the metaheuristic and improves its efficiency. Readers interested in GDE3 should refer to the texts by [ 30 , 31 ].…”

Section: Generalized Differential Evolution Metaheuristicmentioning

confidence: 99%

Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Adekanmbi

Green

2015

The Scientific World Journal

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“…As a result, this procedure reduces the computational costs of the Metaheuristics and improves its efficiency. Readers interested in GDE3 should refer to the texts by [38,39,40].…”

Section: Generalized Differential Evolutionmentioning

confidence: 99%

Portfolio selection problem using generalized differential evolution 3

Adebiyi¹,

Charles²

2015

ams

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This Portfolio selection Problem (PSP) remains an intractable research problem in finance and economics and often regarded as NP-hard problem in optimization and computational intelligence. This paper solved the extended Markowitz meanvariance portfolio selection model with an efficient Metaheuristics method of Generalized Differential Evolution 3 (GDE3). The extended Markowitz meanvariance portfolio selection model consists of four constraints: bounds on holdings, cardinality, minimum transaction lots, and expert opinion. There is no research in literature that had ever engaged the set of four constraints with GDE3 to solve PSP. This paper is the first to conduct the study in this direction. The first three sets of constraints have been presented in other researches in literatures. This paper introduced expert opinion constraint to existing portfolio selection models and solved with GDE3. The computational results obtained in this research study show improved performance when compared with other Metaheuristics methods of Genetic algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Particle Swarm Optimization (PSO).

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“…Based on earlier experience of DE (for example [10] and [3]), the increase in population size decreases the speed of algorithm linearly after reaching a minimum size which is required to solve the problem. Also the required population size is proportional to the problem dimension.…”

Section: Used Control Parameter Setupmentioning

confidence: 99%

An extended mutation concept for the local selection based differential evolution algorithm

Rönkkönen

Lampinen

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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A new mutation concept is proposed to generalize local selection based Differential Evolution algorithm to work in general multimodal problems. Three variations of the proposed method are compared with classic Differential Evolution algorithm using a set of five well known test functions and their variants. The general idea of the new mutation operation is to divide the mutation into two parts: the local and global mutation. The global mutation works as a migration operator allowing the algorithm perform global search efficiently, while the local mutation improves the efficiency of local search.The results show that the concept of global mutation is able to generalize the good performance of local selection based Differential Evolution from convex uni-modal functions to general nonconvex and multi-modal problems. Among the tested functions, the new method was able to outperform the classic Differential Evolution in all but one. A limited analysis of the effects of control parameters to the performance of the algorithm is also done.

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A Comparison of Differential Evolution and Generalized Generation Gap Model

Cited by 5 publications

References 0 publications

Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Portfolio selection problem using generalized differential evolution 3

An extended mutation concept for the local selection based differential evolution algorithm

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