Towards an analysis of dynamic environments

Branke, Jürgen; Salihoğlu, Erdem; Uyar, Şima

doi:10.1145/1068009.1068237

Cited by 56 publications

(35 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [8] the benefit of keeping different aspects of the fitness landscape has been investigated, among others showing that it can be quite beneficial to maintain knowledge about previous local optima.…”

Section: Research Motivationmentioning

confidence: 99%

Particle swarm with speciation and adaptation in a dynamic environment

Branke

Blackwell

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

Self Cite

107

View full text Add to dashboard Cite

This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several proven useful techniques. In particular, SPSO is shown to be able to adapt to a series of dynamic test cases with varying number of peaks (assuming maximization). Inspired by the concept of quantum swarms, this paper also proposes a particle diversification method that promotes particle diversity within each converged species. Our results over the moving peaks benchmark test functions suggest that SPSO incorporating this particle diversification method can greatly improve its adaptability hence optima tracking performance.

show abstract

Section: Research Motivationmentioning

confidence: 99%

Particle swarm with speciation and adaptation in a dynamic environment

Branke

Blackwell

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

Self Cite

107

View full text Add to dashboard Cite

show abstract

“…In our study, we use a dynamic version of the MKP as proposed in [3] and described below. Basis is the first instance given in the file mknapcb4.txt which can be downloaded from [1].…”

Section: The Dynamic Mkpmentioning

confidence: 99%

“…In a dynamic environment, in addition to the (static) characteristics of the fitness landscape, the representation influences the characteristics of the fitness landscape dynamics, as has been recently demonstrated in [3]. Consequently, depending on the representation, the tracking of the optimum over time may be more or less difficult.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Representations in Dynamic Knapsack Problems

Branke

Orbayı

Uyar

2006

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract.The effect of different representations has been thoroughly analyzed for evolutionary algorithms in stationary environments. However, the role of representations in dynamic environments has been largely neglected so far. In this paper, we empirically compare and analyze three different representations on the basis of a dynamic multi-dimensional knapsack problem. Our results indicate that indirect representations are particularly suitable for the dynamic multi-dimensional knapsack problem, because they implicitly provide a heuristic adaptation mechanism that improves the current solutions after a change.

show abstract

“…In the real world, many optimization problems are time-varying 1,2 . In fact, the challenge of these optimization problems consists in locating and tracking the moving optima over time.…”

Section: Introductionmentioning

confidence: 99%

Parameters control in GAs for dynamic optimization

Jebari¹,

Bouroumi²,

Ettouhami³

2013

IJCIS

View full text Add to dashboard Cite

The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters. Yet the control of parameters has received much attention in the case of static optimization problems, its investigation in the case of dynamic optimization problems (DOPs) is certainly a promising area of search. Indeed, in the case of DOPs the problem is not just to find the optima but to track the moving optima over time, so the parameters must be adapted to this dynamic environment. The proposed algorithm Parameters Control for Dynamic Optimization (PCDO) is based on Genetic Algorithm with Fitness Sharing (GAFS). To solve DOPs by controlling GAs parameters, PCDO uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optimums and to perform GAFS. Second, a modified enthusiasm selection is used to adjust the selection pressure. Third, a clustering multi non uniform Mutation is utilized to locate an unexplored search space. Fourth, a novel technique with multiple crossover is applied to guide the algorithm in promising regions of the search space. Fifth, a self adaptive mutation rate is evolved through generation with a learned parameter, in order to control the diversity of the population. In the concern of maintaining the diversity of the population, a new genetic operator called Fertilization is proposed. PCDO is tested on six problems generated from Generalized Dynamic Benchmark Generator (GDBG). Experimental results demonstrate that PCDO outperforms other GAs on DOPs. Moreover, the ability of PCDO to maintain diversity is demonstrated by a new diversity measure.

show abstract

Towards an analysis of dynamic environments

Cited by 56 publications

References 24 publications

Particle swarm with speciation and adaptation in a dynamic environment

Particle swarm with speciation and adaptation in a dynamic environment

The Role of Representations in Dynamic Knapsack Problems

Parameters control in GAs for dynamic optimization

Contact Info

Product

Resources

About