A Multi-objective Genetic Algorithm Based Approach to the Optimization of Oligonucleotide Microarray Production Process

DOI: 10.1007/978-3-540-85984-0_125

ABSTRACT Microarrays are becoming more and more utilized in the experimental platform in molecular biology. Although rapidly becoming
affordable, these micro devices still have quite high production cost which limits their commercial appeal. Here we present
a novel multiobjective evolutionary approach to the optimization of the production process of microarray devices mainly aimed
at lowering the number of fabrication steps. In order to allow the reader to better understand what we describe we report
herein a detailed description of a real-world study case carried out on the most recent microarray platforms of the market
leader in this field. A comparative analysis of the most widely used approaches, main potentialities and drawbacks of the
proposed approach are presented.

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    • "4.5. The novel multi-objective constrained optimisation algorithm MOGA (Menolascina et al. 2008) extend the potentiality of GA to the case of multiple targets problems. GA are inspired by the evolutionist theory explaining the origin of species and, consequently, they require a terminology deriving from genetics. "
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    ABSTRACT: 2012): Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms The paper addresses the optimal design of distribution networks (DNs). Considering a distributed system composed of stages connected by material links labelled with suitable performance indices, a procedure employing multi-objective genetic algorithms (MOGAs) is presented to select the optimal DN configuration. The paper enhances a deterministic procedure for DN strategic configuration by employing MOGACOP, a real-valued chromosome MOGA that can be applied to the case of constrained nonlinear function. The main MOGA characteristics are the presence of three populations: two reference sets of individuals satisfying all constraints, namely, a set of Pareto optimal individuals (frontier population) and a set of individuals covering the previous population (archive population), together with a search set which, on the contrary, includes individuals that are allowed to not satisfy all constraints (laboratory population). MOGACOP allows solving the DN design nonlinear problem, which exhibits a multi-objective function that varies linearly only with some variables and nonlinearly with the remaining variables. The proposed MOGA application allows finding a Pareto frontier of optimal solutions, which is compared with the frontier obtained by solving the same problem with Integer Linear Programming (ILP), where piecewise constant contributions are linearly approximated. The two found curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimisation and the ILP models are applied under structural constraints to a case study describing the distribution chain of a large enterprise of southern Italy producing consumer goods.
    International Journal of Computer Integrated Manufacturing 12/2012; 25(12-25):1139-1150. DOI:10.1080/0951192X.2012.684719 · 1.01 Impact Factor
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    ABSTRACT: In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions. KeywordsMultiobjective optimization-Evolutionary algorithms-Pareto optimal solution-Linear fitness function
    Journal of Control Theory and Applications 11/2010; 8(4):533-539. DOI:10.1007/s11768-010-8239-3
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    ABSTRACT: In this paper we propose a new implementation of a multi objective genetic algorithm that handles constrained problems to approach the financial problem of the portfolio optimization. The objective of the paper is to propose and empirically apply a new multi-objective genetic algorithm for portfolio optimization extending the Markowitz mean-variance model ([1,2] Markowitz, 1952 and 1959). At the end of the paper the obtained results are discussed and compared with non linear other different techniques.
    Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers; 01/2011
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