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In this paper, a new biclustering analysis toolbox called BAT, which is based on the BiHEA (Biclustering via a Hybrid Evolutionary Algorithm), is presented. The BiHEA is a memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with a local search technique in order to perform microarray biclustering. This method simultaneously considers several goals for optimization, giving as a result a set of biclusters that present a satisfactory trade-off between all of them. The novel software introduced in this article provides the possibility of running the BiHEA along with several pre-processing facilities for the input data and different visualization and statistical tools for the analysis of the biclusters. [ABSTRACT FROM AUTHOR]

In this paper, a new memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with local search for microarray biclustering is presented. The original features of this proposal are the consideration of opposite regulation and incorporation of a mechanism for tuning the balance between the size and row variance of the biclusters. The approach was developed according to the Platform and Programming Language Independent Interface for Search Algorithms (PISA) framework, thus achieving the possibility of testing and comparing several different memetic MOEAs. The performance of the MOEA strategy based on the SPEA2 performed better, and its resulting biclusters were compared with those obtained by a multi-objective approach recently published. The benchmarks were two datasets corresponding to Saccharomyces cerevisiae and human B-cells Lymphoma. Our proposal achieves a better proportion of coverage of the gene expression data matrix, and it also obtains biclusters with new features that the former existing evolutionary strategies can not detect. [ABSTRACT FROM AUTHOR]

In this paper a new hybrid approach that integrates an evolutionary algorithm with local search for microarray biclustering is presented. The novelty of this proposal is constituted by the incorporation of two mechanisms: the first one avoids loss of good solutions through generations and overcomes the high degree of overlap in the final population; and the other one preserves an adequate level of genotypic diversity. The performance of the memetic strategy was compared with the results of several salient biclustering algorithms over synthetic data with different overlap degrees and noise levels. In this regard, our proposal achieves results that outperform the ones obtained by the referential methods. Finally, a study on real data was performed in order to demonstrate the biological relevance of the results of our approach. [ABSTRACT FROM AUTHOR]

In this work a memetic algorithm for the Bus Network Scheduling Problem (BNSP) is presented. The algorithm comprises two stages: the first one calculates the distance among all the pairs of bus stops, and the second one is a MOEA that uses a novel simulation procedure for the calculus of the fitness function. This simulation method was specially developed for the BNSP. The EA used for the second stage was selected between the IBEA, NSGA-II and SPEA2 by means of some PISA tools. As a result of this experimentation, the SPEA2 was preferred since it presents the more spread solution set. [ABSTRACT FROM AUTHOR]

In this work we present a critical analysis of three novel parallel-distributed implementations of a multi-objective genetic algorithm (pdGAs) for instrumentation design applications. The pdGAs aim at establishing a sensible configuration of sensors for the initialization of instrumentation design studies of industrial processes. They were built on the basis of an evolutionary island model, the master-worker paradigm, and different migration and parameter control policies. The performance of the resulting implementations was assessed by testing algorithmic behavior on an industrial example that corresponds to an ammonia synthesis plant. The three pdGAs' results were highly satisfactory in terms of speed-up, efficiency and instrumentation quality, thus revealing to constitute competitive tools with strong potential for their use in the industrial area. As well, from an overall point of view, the pdGA version with adaptive parameter control represents the best implementation's alternative. Keywords: Genetic Algorithms, Distributed Computing, Instrumentation Design. [ABSTRACT FROM AUTHOR]

In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI's special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm. Keywords: Combinatorial Optimization Problem, PSE, Process-Plant Instrumentation Design, Multi-Objective Genetic Algorithm, Observability Analysis. [ABSTRACT FROM AUTHOR]