A potentially large number of molecular markers are available for identifying genotypes in various species. For wheat, cultivar identity is an important determinant for end-use segregation and for payment of end-point...
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Many optimal scheduling and resource allocation problems involve large number of integer variables and the resulting optimization problems become integer linear programs (ILPs) having a linear objective function and l...
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ISBN:
(纸本)3540223444
Many optimal scheduling and resource allocation problems involve large number of integer variables and the resulting optimization problems become integer linear programs (ILPs) having a linear objective function and linear inequality/equality constraints. The integer restrictions of variables in these problems cause tremendous difficulty for classical optimization methods to find the optimal or a near-optimal solution. The popular branch-and-bound method is an exponential algorithm and faces difficulties in handling ILP problems having thousands or tens of thousands of variables. In this paper, we extend a previously-suggested customized CA with four variations of a multi-parent concept and significantly better results are reported. We show variations in computational time and number of function evaluations for 100 to 100,000-variable ILP problems and in all problems a near-linear complexity is observed. The exploitation of linearity in objective function and constraints through genetic crossover and mutation operators is the main reason for success in solving such large-scale applications. This study should encourage further use of customized implementations of EAs in similar other applications.
Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowada...
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Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game -theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of integer linear programs that were known to be tractable so far.
Thanks to the rapidly advancing development of (commercial) MILP software and hardware components, pseudo-polynomial formulations have been established as a powerful tool for solving cutting and packing problems in re...
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Thanks to the rapidly advancing development of (commercial) MILP software and hardware components, pseudo-polynomial formulations have been established as a powerful tool for solving cutting and packing problems in recent years. In this paper, we focus on the one-dimensional skiving stock problem (SSP), where a given inventory of small items has to be recomposed to obtain a maximum number of larger objects, each satisfying a minimum threshold length. In the literature, different modeling approaches for the SSP have been proposed, and the standard flow-based formulation has turned out to lead to the best trade-off between efficiency and solution time. However, especially for instances of practically meaningful sizes, the resulting models involve very large numbers of variables and constraints, so that appropriate reduction techniques are required to decrease the numerical efforts. For that reason, this paper introduces two improved flow-based formulations for the skiving stock problem that are able to cope with much larger problem sizes. By means of extensive experiments, these new models are shown to possess significantly fewer variables as well as an average better computational performance compared to the standard arcflow formulation. (C) 2019 Elsevier Ltd. All rights reserved.
A society graph, as considered by [Faliszewski et al., IJCAI 2018], is a graph corresponding to an election instance where every possible ranking is a node, and the weight of such a node is given by the number of vote...
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ISBN:
(纸本)9798400704864
A society graph, as considered by [Faliszewski et al., IJCAI 2018], is a graph corresponding to an election instance where every possible ranking is a node, and the weight of such a node is given by the number of voters whose vote correspond to the said ranking. A natural diffusion process on this graph is defined, and an immediate question that emerges is whether there is a diffusion path that leads to a particular candidate winning according to a certain voting rule-this turns out to be *** this contribution, we consider the setting when votes are approval ballots, as opposed to rankings-and we consider both the possible and necessary winner problems. We demonstrate that it is possible to efficiently determine if a candidate is a possible winner (i.e, if there exists a diffusion path along which a given candidate wins the election) if the underlying society graph is a star (i.e, tree of diameter at most two), while the problem is NP-complete for trees of diameter d for d > 2. Analogously, we show that it is possible to efficiently determine if a candidate is a necessary winner (i.e, a winner for every possible diffusion path) if the underlying society graph is a star, while the problem is coNP-complete for trees of diameter d for d > 2. We also show the following results on structured graphs for the possible winner problem: the problem is strongly NP-complete on a disjoint union of paths, and on trees of constant diameter. We also report preliminary experiments from an ILP-based implementation.
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