Vehicle driving consumes time and energy (fuel, electricity etc.). Usually both have to be minimized. Minimizing the consumption of one of them leads to increasing the consumption of the other. To find driving strategies that take into consideration both objectives, we have implemented a multiobjective genetic algorithm that constructs driving strategies as sets of rules. Optimal sets of rules ...

In this paper, we examine the multicriteria optimization involved in scheduling for data-path synthesis (DPS). The criteria we examine are the area cost of the components and schedule time. Scheduling for DPS is a well-known NPcomplete problem. We present a method to find nondominated schedules using a combination of restricted search and heuristic scheduling techniques. Our method supports des...

In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi-objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well-known, general-purpose, multi-objective evolutionary algorithms to learn Boolean queries in IRSs. These evo...

The paper concerns the design of evolutionary algorithms and pattern search methods on two circuit design problems: the multi-objective optimization of an Operational Transconductance Amplifier and of a fifth-order leapfrog filter. The experimental results obtained show that evolutionary algorithms are more robust and effective in terms of the quality of the solutions and computational effort t...

This paper resolves a question proposed in Kardaras and Robertson [Ann. Appl. Probab. 22 (2012) 1576–1610]: how to invest in a robust growthoptimal way in a market where precise knowledge of the covariance structure of the underlying assets is unavailable. Among an appropriate class of admissible covariance structures, we characterize the optimal trading strategy in terms of a generalized versi...

Distant supervision is a scheme to generate noisy training data for relation extraction by aligning entities of a knowledge base with text. In this work we combine the output of a discriminative at-least-one learner with that of a generative hierarchical topic model to reduce the noise in distant supervision data. The combination significantly increases the ranking quality of extracted facts an...

We provide a comprehensive overview of the literature algorithmic approaches for multiobjective mixed-integer and integer linear optimization problems. More precisely, we categorize display exact methods problems with variables computing entire set nondominated images. Our review lists 108 articles is intended to serve as reference all researchers who are familiar basic concepts have an interes...

This article presents an efficient parametric optimization method for the biobjective optimal routing problem. The core process is a bounded greedy singleobjective shortest path approximation algorithm. This method avoids the computationally intensive dominance check with labeling methods and overcomes the deficiency with existing parametric methods that can only find extreme nondominated paths...

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FPGA). FPGA uses a new ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally expensive. New genetic operators are employed to enhance the algorithm’s performance in terms of convergence behavior and computational effort. Compu...