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JournalISSN: 1570-1166

Journal of Mathematical Modelling and Algorithms 

Springer Science+Business Media
About: Journal of Mathematical Modelling and Algorithms is an academic journal. The journal publishes majorly in the area(s): Metaheuristic & Local search (optimization). It has an ISSN identifier of 1570-1166. Over the lifetime, 338 publications have been published receiving 6045 citations.


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Journal ArticleDOI
TL;DR: A number of different clustering algorithms are applied to the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest.
Abstract: Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.

501 citations

Journal ArticleDOI
TL;DR: An ant colony optimization approach that uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions is developed, which is the first competitive ant colonies optimization approach for job shop scheduling instances.
Abstract: We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.

240 citations

Journal ArticleDOI
TL;DR: A new diversity measure which is called Pairwise Failure Crediting (PFC) is proposed, which forms one of the two evolutionary pressures being exerted explicitly in DIVACE.
Abstract: Multi-objective evolutionary algorithms for the construction of neural ensembles is a relatively new area of research. We recently proposed an ensemble learning algorithm called DIVACE (DIVerse and ACcurate Ensemble learning algorithm). It was shown that DIVACE tries to find an optimal trade-off between diversity and accuracy as it searches for an ensemble for some particular pattern recognition task by treating these two objectives explicitly separately. A detailed discussion of DIVACE together with further experimental studies form the essence of this paper. A new diversity measure which we call Pairwise Failure Crediting (PFC) is proposed. This measure forms one of the two evolutionary pressures being exerted explicitly in DIVACE. Experiments with this diversity measure as well as comparisons with previously studied approaches are hence considered. Detailed analysis of the results show that DIVACE, as a concept, has promise.

166 citations

Journal ArticleDOI
TL;DR: This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands (VRPSD) and explores the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality.
Abstract: This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands (VRPSD). The problem is known to have a computationally demanding objective function, which could turn to be infeasible when large instances are considered. Fast approximations of the objective function are therefore appealing because they would allow for an extended exploration of the search space. We explore the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality. Particularly helpful for some metaheuristics is the objective function derived from the traveling salesman problem (TSP), a closely related problem. In the light of this observation, we analyze possible extensions of the metaheuristics which take the hybridized solution approach VRPSD-TSP even further and report about experimental results on different types of instances. We show that, for the instances tested, two hybridized versions of iterated local search and evolutionary algorithm attain better solutions than state-of-the-art algorithms.

164 citations

Journal ArticleDOI
TL;DR: This work analyzes simple EAs on well-known problems, namely sorting and shortest paths, and finds that sorting is the maximization of “sortedness” which is measured by one of several well- known measures of presortedness.
Abstract: The analysis of evolutionary algorithms is up to now limited to special classes of functions and fitness landscapes. E.g., it is not possible to characterize the set of TSP instances (or another NP-hard combinatorial optimization problem) which are solved by a generic evolutionary algorithm (EA) in an expected time bounded by some given polynomial. As a first step from artificial functions to typical problems from combinatorial optimization, we analyze simple EAs on well-known problems, namely sorting and shortest paths. Although it cannot be expected that EAs outperform the well-known problem specific algorithms on these simple problems, it is interesting to analyze how EAs work on these problems. The following results are obtained: - Sorting is the maximization of “sortedness” which is measured by one of several well-known measures of presortedness. The different measures of presortedness lead to fitness functions of quite different difficulty for EAs. - Shortest paths problems are hard for all types of EA, if they are considered as single-objective optimization problems, whereas they are easy as multi-objective optimization problems.

142 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
201525
201432
201325
201223
201123
201023