The bi-objective travelling salesman problem with profits and its connection to computer networks.

This is an interdisciplinary work in Computer Science and Operational Research. As it is well known, these two very important research fields are strictly connected. Among other aspects, one of the main areas where this interplay is strongly evident is Networking. As far as most recent decades have seen a constant growing of every kind of network computer connections, the need for advanced algorithms that help in optimizing the network performances became extremely relevant. Classical Optimization-based approaches have been deeply studied and applied since long time. However, the technology evolution asks for more flexible and advanced algorithmic approaches to model increasingly complex network configurations. In this thesis we study an extension of the well known Traveling Salesman Problem (TSP): the Traveling Salesman Problem with Profits (TSPP). In this generalization, a profit is associated with each vertex and it is not necessary to visit all vertices. The goal is to determine a route through a subset of nodes that simultaneously minimizes the travel cost and maximizes the collected profit. The TSPP models the problem of sending a piece of information through a network where, in addition to the sending costs, it is also important to consider what “profit” this information can get during its routing. Because of its formulation, the right way to tackled the TSPP is by Multiobjective Optimization algorithms. Within this context, the aim of this work is to study new ways to solve the problem in both the exact and the approximated settings, giving all feasible instruments that can help to solve it, and to provide experimental insights into feasible networking instances

A two-phase method for bi-objective combinatorial optimization and its application to the TSP with profits

We study a variant of the two-phase method for general bi-objective combinatorial optimization problems. First, we analyze a basic enumerative procedure, often used in literature to solve specific bi-objective combinatorial optimization problems, making it suitable to solve general problems. We show that the procedure generates the exact set E of efficient points by solving exactly 2[notdef]E[notdef] − 1 single objective problems. Second, we embed the procedure in a classic two-phase framework, where supported points are computed in the first phase and unsupported points are computed in the second phase. We test the refined approach on a hard problem, namely the Traveling Salesman Problem with Profits, a bi-objective generalization of the well known Traveling Salesman Problem. On the tested instances, the procedure outperforms the [epsilon1]-constraint method, one of the most used approaches to solve exactly general bi-objective combinatorial optimization problems

A two-phase method for bi-objective combinatorial optimization and its application to the TSP with profits

We study a variant of the two-phase method for general bi-objective combinatorial optimization problems. First, we analyze a basic enumerative procedure, often used in literature to solve specific bi-objective combinatorial optimization problems, making it suitable to solve general problems. We show that the procedure generates the exact set E of efficient points by solving exactly 2[notdef]E[notdef] − 1 single objective problems. Second, we embed the procedure in a classic two-phase framework, where supported points are computed in the first phase and unsupported points are computed in the second phase. We test the refined approach on a hard problem, namely the Traveling Salesman Problem with Profits, a bi-objective generalization of the well known Traveling Salesman Problem. On the tested instances, the procedure outperforms the [epsilon1]-constraint method, one of the most used approaches to solve exactly general bi-objective combinatorial optimization problems

Genome-wide selection in apple: A pilot study in European breeding programs

The tremendous increase in throughput of genotyping techniques opened appealing perspectives for genome-wide selection (GWS), which could enhance breeding efficiency by decreasing generation interval and increasing selection intensity and/or accuracy of breeding values. In GWS a large training population with both phenotypic and genotypic data is used to construct a statistical prediction model which is then applied to estimate Genomic Breeding Values (GBV) of individuals that only have genotypic data. In the EU-FP7 project FruitBreedomics, we performed a pilot study of GWS in apple. The two main objectives were to provide proof of principle and to evaluate the accuracy of prediction with respect to the relatedness between the test and the training populations. Hereto the phenotypic means of the 50 best and 50 worst predicted individuals of a test progeny comprising 700 individuals were compared and correlations between predicted GBV and phenotypic data were compared for smaller full-sib families of different relatedness.The training population included 20 full-sib families comprising 992 individuals genotyped with an Illumina 20K SNP array and phenotyped for fruit quality traits. The test population comprised four progenies from commercial breeding programs totaling 1500 individuals that were genotyped with 512 SNP that had been selected among the 20K for heterozygosity in the parents of the test population and genome-wide coverage. SNP genotypes were completed through imputation and fed into the calibrated prediction model to obtain GBV on the 1500 offspring. We will present the results and discuss challenges remaining to bring GWS into practice in Rosaceae species

Genomic selection in apple: a multiple years pilot study on quantitative and ordinal traits

Genomic selection in apple: a multiple years pilot study on quantitative and ordinal traits. 14. International Eucarpia Fruit Breeding and Genetics Symposiu