Using Datamining Techniques to Help Metaheuristics: A Short Survey

Jourdan, Laëtitia; Dhaenens, Clarisse; Talbi, El‐Ghazali

doi:10.1007/11890584_5

Cited by 45 publications

(22 citation statements)

References 31 publications

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“…While in the former the techniques are applied one after another (each using the output of the previous as its input), the latter represents cooperative optimization models. In [25], authors describe applications of data mining techniques to help metaheuristics. Finally, a survey on the integration of machine learning in evolutionary computation can be found in [26].…”

Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

“…Our work builds on the classification in [25] and extends it by proposing more categories and analyzing a higher number of works. In our view, the classification in [25] is more suitable for works where machine learning is employed to enhance metaheuristics than the one presented in [3], which was designed to be more general and to include other hybridizations.…”

Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

“…In our view, the classification in [25] is more suitable for works where machine learning is employed to enhance metaheuristics than the one presented in [3], which was designed to be more general and to include other hybridizations. In particular, works are classified into specifically-located hybridizations (where machine learning is applied in a specific procedure) and global hybridizations (in which machine learning has a higher effect on the metaheuristic design).…”

Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

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Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

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Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

Section: Reviews On the Combination Of Metaheuristics And Machine Leamentioning

confidence: 99%

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Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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“…Incorporating knowledge into operators using Meta data will improve exploiting as well as exploring interesting areas in the search space (Jourdan et al 2006) and hence enhance the performance. Also Meta heuristics can enhance mining a diverse set of rules.…”

Section: Meta Data and Meta Heuristicsmentioning

confidence: 99%

Evolutionary multi objective optimization for rule mining: a review

Srinivasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.

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“…In general, net-61 work design problems are reported as NP-Hard problems (Kiu & 62 McAllister, 1998). Meta-heuristics have received considerable 63 attention and are considered as effective ways to search the design 64 space and find the most reliable design (Dengiz,Altiparmak,& 85 Other heuristic methods, such as simulated annealing (SA), tabu 86 search and particle swarm, have been used to address network 87 design problems ( (Jourdan, Dhaenens, & Talbi, 2006). 98 In recent years, mathematical models have been used to solve design cost that can be used 150 as an indicator of the distance or any variable showing link inter-151 estingness for selection.…”

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confidence: 99%