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A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of the largest networks evaluated to date by genetic algorithms applied to network community analysis. Rather than using GA to decide community structure that simply maximizes modularity of a network, as is typical, we use two...
In this study, we present the Big Bang-Big Crunch (BB-BC) method to solve the post-enrolment course timetabling problem. This method is derived from one of the evolution of the universe theories in physics and astronomy. The BB-BC theory involves two phases (Big Bang and Big Crunch). The Big Bang phase feeds the Big Crunch phase with many inputs and the Big Crunch phase is the shrinking destiny of...
The search for the global minimum of a potential energy function is very difficult since the number of local minima grows exponentially with the molecule size. The present work proposes the application of genetic algorithm and tabu search methods, which called GAMCP (Genetic Algorithm with Matrix Coding Partitioning), and TSVP (Tabu Search with Variable Partitioning), respectively, for minimizing...
In this research, we introduce a stratified random sampling technique that guides the selection mechanism to select the events (exams) for the integrated two-stage multi-neighbourhood tabu search (ITMTS) in solving examination timetabling problem. This technique is used during the timetable improvement phase especially when dealing with the exhaustive search mechanism in order to reduce the possibilities...
Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving...
Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known...
Advances in DNA microarray technology has motivated the research community to introduce sophisticated techniques for analyzing the resulted large-scale datasets. Biclustering techniques have been widely adapted for analyzing microarray gene expression data due to its ability to extract local patterns with a subset of genes that are similarly expressed over a subset of samples. Mostly, biclustering...
Evolutionary algorithms are a frequently used technique for designing morphology and controller of a robot. However, a significant challenge for evolutionary algorithms is premature convergence to local optima. Recently proposed Novelty Search algorithm introduces a radical idea that premature convergence to local optima can be avoided by ignoring the original objective and searching for any novel...
In this paper a new criterion is introduced for the discrete covering problem. Using the representation of a possibility measure through associated probabilities, a new criterion for discrete covering problem is constructed based on aggregation by the Monotone Expectation (ME) (or Choquet integral). In this criterion the a priori information represented by a possibility measure and a misbelief distribution...
The generalized assignment problem is a well-known NP-complete problem whose objective is to find a minimum cost assignment of a set of jobs to a set of agents by considering the resource constraints. Dynamic instances of the generalized assignment problem can be created by changing the resource consumptions, capacity constraints and costs of jobs. Memory-based approaches are among a set of evolutionary...
This work utilize Round Robin (RR) mechanism to systematically explore neighbors of solution. RR is one of the simplest scheduling algorithms, which assigns time slices to each process in equal portions and in circular order handling all processes without priority. In this work, we consider five different neighborhood structures. RR gives each neighborhood a certain number of iterations to explore...
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