On the use of niching for dynamic landscapes

Cedeño, Walter; Vemuri, V. Rao

doi:10.1109/icec.1997.592336

Cited by 54 publications

(42 citation statements)

References 4 publications

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“…Typical examples are the random immigrants scheme [9] , sharing or crowding mechanisms [24] , thermodynamic GA [25,26] , and hypermutation [11] . The random immigrants approach is quite simple and natural.…”

Section: The Random Immigrants Schemementioning

confidence: 99%

A hybrid immigrants scheme for genetic algorithms in dynamic environments

Yang

Tinós

2007

Int J Automat Comput

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Abstract:Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

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Section: The Random Immigrants Schemementioning

confidence: 99%

A hybrid immigrants scheme for genetic algorithms in dynamic environments

Yang

Tinós

2007

Int J Automat Comput

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“…The methods that use a group of sub-populations distributed on different optima (refer to Oppacher & Wineberg (1999), Cedeno & Vemuri (1997) and Ursem (2000) ), thus increasing the probability to find new optima. Guntsch & Middendorf (2002a) two dynamic discrete combinatorial problems, the dynamic traveling salesman problem (TSP), and the dynamic quadratic assignment problem, with a modified ant colony algorithm.…”

Section: Some Techniques Aimed At Dynamic Optimizationmentioning

confidence: 99%

Continuous Dynamic Optimization

Tfaili¹

2011

Ant Colony Optimization - Methods and Applications

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“…So far, the studies of applying evolutionary computation to dynamic environments have focused on two main approaches: Memory recall [5], [4] and promoting diversity [13], [7]. The idea of the first method is to equip algorithms with a mechanism to recall previous solutions, and the motivation for the second method is to prevent the premature convergence of the population of a searcher resulting in its failure to discover new areas of the search space.…”

Section: B Dynamic Environmentmentioning

confidence: 99%