Implementing metaheuristic optimization algorithms with JECoLi

This work proposes JECoLi - a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency. The project is opensource, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (http://darwin.di.uminho.pt/jecoli). JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization

A software platform for evolutionary computation with pluggable parallelism and quality assurance

This paper proposes the Java Evolutionary Computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming language. JECoLi aims to offer a solution suited for the integration of Evolutionary Computation (EC)-based approaches in larger applications, and for the rapid and efficient benchmarking of EC algorithms in specific problems. Its main contributions are (i) the implementation of pluggable parallelization modules, independent from the EC algorithms, allowing the programs to adapt to the available hardware resources in a transparent way, without changing the base code; (ii) a flexible platform for software quality assurance that allows creating tests for the implemented features and for user-defined extensions. The library is freely available as an open-source project.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA-EIA/115176/2009, Programa COMPET

An integrated framework for strain optimization

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/).This work is partially funded by ERDF- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP- 01-0124-FEDER-015079 and PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009

Tools for traffic engineering on IP networks

In this work, an user friendly software application is proposed, built on top of a network optimization framework, aiming to make traffic engineering an easier task for IP network administrators. This framework was developed in the Center of Computer Science and Technology (CCTC) of the University of Minho and allows the improvement of quality of service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols, such as OSPF. This goal is achieved mainly using Evolutionary Algorithms as the optimization engines, while networks are represented using graph-based mathematical models. These methods allow the optimization of distinct cost functions, using penalties that take into account several measures of network performance such as network congestion and average end-to-end delays. The main goal of this work is to create a structured graphical user interface to support the optimization framework, enabling the user to simulate the effects of diferente OSPF settings, to obtain highly optimized configurations and to compare different weight setting optimization methods

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios

Comparison of pathway analysis and constraint-based methods for cell factory design

Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs.“DeYeastLibrary – Designer yeast strain library optimized for metabolic engineering applications”, Ref.ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES, DD-DeCaf and SHIKIFACTORY100, both funded by the European Union through the Horizon 2020 research and innovation programme (grant agreements no. 686070 and 814408). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors acknowledge the use of computing facilities within the scope of the Search-ON2: Revitalization of HPC infrastructure of UMinho” project (NORTE-07-0162-FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF). VV also thanks funding from FCT/MCTES for the PhD studentship with reference SFRH/BD/118657/2016.info:eu-repo/semantics/publishedVersio

Email spam detection : a symbiotic feature selection approach fostered by evolutionary computation

Post-print version (prior to journal publication)The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet service. In this perspective, this work proposes and explores the concept of a novel symbiotic feature selection approach allowing the exchange of relevant features among distinct collaborating users, in order to improve the behavior of anti-spam filters. For such purpose, several Evolutionary Algorithms (EA) are explored as optimization engines able to enhance feature selection strategies within the anti-spam area. The proposed mechanisms are tested using a realistic incremental retraining evaluation procedure and resorting to a novel corpus based on the well-known Enron datasets mixed with recent spam data. The obtained results show that the proposed symbiotic approach is competitive also having the advantage of preserving end-users privacy.The work of P. Cortez and P. Sousa was funded by FEDER, through the program COMPETE and the Portuguese Foundation for Science and Technology (FCT), within the project FCOMP-01-0124-FEDER-022674

Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects Ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/ES/UI0752/2011

Exploração de paralelismo massivo em algoritmos evolucionários

Dissertação de mestrado em Engenharia InformáticaEsta dissertação está centrada na paralelização massiva da biblioteca Java Evolutionary Cornputation Library), JECoLi, que se foca no desenvolvimento de meta-heurísticas de otimização(e.g. Algoritmos Evolucionários (AEs)) na linguagem Java. Os AEs são um paradigma da Computação Evolucionária (CE) utilizados para resolver problemas complexos através de um método iterativo que evolui um conjunto de soluções (população) tendo em conta os princípios da teoria de evolução por seleção natural apresentada por Charles Darwin. Estes algoritmos estão divididos em duas categorias, AEs não estruturados e AEs estruturados. Os AEs não estruturados são caracterizados por uma população centralizada onde existe apenas um conjunto de soluções ao qual é aplicado o processo evolutivo. Por outro lado, os AEs estruturados contêm várias populações onde os processos evolutivos são conduzidos de forma independente, embora existindo troca de informação. Os algoritmos de ambas as categorias podem ser paralelizados de diferentes maneiras. Nesta dissertação, foram implementadas quatro versões paralelas da plataforma JECoLi de forma o menos invasiva possível, tendo em conta modelos paralelos já formulados: um modelo de paralelismo global; um modelo de ilhas em ambiente de memória partilhada; um modelo de ilhas em ambiente de memória distribuída; e um modelo híbrido. Estas implementações paralelas foram executadas no cluster Services and Advanced Research Cornputing with HTC/HPC clusters (SeARCH) utilizando o máximo de recursos computacionais possíveis de modo a realizar uma posterior análise dos resultados obtidos. Foram utilizados dois casos de estudo reais para validar as implementações paralelas, um problema de otimização de um bioprocesso de fermentação fed-batch e outro de otimização dos pesos de um protocolo de encaminhamento (OSP F). Cada uma das implementações paralelas foi testada nos dois casos de estudo, aplicando o máximo de paralelismo possível tendo em conta as limitações de cada caso de estudo, dos modelos paralelos e dos recursos disponíveis. Com estes testes concluí-se uma boa escalabilidade destes algoritmos, onde se destacam as implementações relativas ao modelo de ilhas em memória distribuída e ao modelo híbrido. Contudo, algumas configurações que originam maiores ganhos foram descartadas pois não produzem valores de aptidão aceitáveis.This dissertation is centered in the massive parallelization of the Java Evolutionary Computation Library, JECoLi, which focuses on the development of meta-heuristics optimizations (e.g. Evolutionary Algorithms (EAs)) in the Java programming language. The EAs are a paradigm of Evolutionary Computation (EC) used to solve complex problems through an iterative method that evolves a set of solutions (population) taking into account the principles of the theory of evolution by natural selection by Charles Darwin. These algorithms are divided into two categories, unstructured EAs and structured EAs. The unstructured AEs are characterized by a centralized population where there is only one set of solutions for which the iterative method is applied. On the other hand, structured AEs contain several independent populations where evolutionary processes are applied, although there exchange information. The algorithms of both categories can be parallelized in different ways. ln this dissertation, it was implemented four parallel versions of the platform JECoLi in a less invasive way, taking into account parallel models already formulated: a global parallelism model; a island model in shared memory environment; a island mo del in distributed memory environment; and a hybrid model. These parallel implementations were executed in the cluster SeARCH using the maximum computing resources in order to perform a further analysis of the results obtained. Two case studies were used to validate the parallel implementations, a bioprocess optirnization problem of fed-batch fermentation and other weights optimization problem of a routing protocol (OSPF). Each of the parallel implementations were tested in the two case studies, applying the maximum parallelism taking into account the limitations of each case studies, parallel models and available resources. With these tests can be concluded a good scalability of these algorithms, which highlights the implementations on the island model in distributed memory and hybrid model. However, some settings that give greater gains were discarded because they produce no acceptable fitness values

Algorithms and tools for in silico design of cell factories

PhD thesis in BioengineeringThe progressive shift from chemical to biotechnological processes is one of the pillars of the 21st century industrial biotechnology. Projections from the Organization for Economic Co-operation and Development estimate that, within the next two decades, about 35% of the production of chemicals will be guaranteed by biotechnological processes. The development of efficient cell-factories, capable of outperforming current chemical processes, is vital for this leap to happen. The development of constraint-based models of metabolism and rational computational strain optimization algorithms (CSOMs) hold the promise to accelerate these e orts. Here, we aim to provide an in depth and critical review of the currently available constraint-based CSOMs, their strengths and limitations, as well as to discuss future trends in the field. Then, we cover in detail the main tasks in strain design and provide a taxonomy of the main CSOMs. These are presented in detail and their features and limitations are explored. One of the identified problems is their limited offering of trade-o solutions of biotechnological objectives (e.g. overproducing desired compounds or minimizing the cost of solutions) versus cellular objectives (e.g. maximizing biomass). To tackle this problem we developed an evolutionary multi-objective (MO) framework for strain optimization capable of finding high-quality, trade-off solutions that can be explored by metabolic engineering experts. Also, the majority of the strain optimization algorithms rely on phenotype prediction methods based on debatable biological assumptions. We verified that, for a large percentage of solutions generated by a CSOM using one phenotype prediction method, the results would not hold when simulated with an alternative method. Leveraging on the previously developed framework and driven by the MO nature of this problem, we proposed a tandem approach capable of finding strain designs that comply with the assumptions of distinct phenotype prediction methods, validating the approach with multiple case studies. Finally, all the algorithms developed during this work are made available in the form of an open and flexible software framework. This framework is a powerful tool for both common users, interested in exploring the available methods, and experienced programmers which are able to easily extend it to support new features.A conversão de processos químicos em processos biotecnológicos e um dos grandes objetivos da biotecnologia industrial para o seculo XXI. A Organização para a Cooperação e Desenvolvimento Economico estima que, nas próximas duas décadas, cerca de 35% da produção de compostos químicos sejam assegurados por processos biotecnológicos. O desenvolvimento de fabricas celulares eficientes, capazes de superar o rendimento dos atuais processos químicos, é vital para que este avanço seja possível. O desenvolvimento de modelos metabólicos e algoritmos para otimização de estirpes (AOEs), e uma das grandes esperanças para acelerar estes esforços. Neste trabalho, pretendemos efetuar uma revisão aprofundada dos AOEs atuais baseados na modelação por restrições, analisar os seus pontes fortes e limitações, e discutir temas de interesse futuro na área. De seguida, estudamos em detalhe os tipos de estratégias comuns para o desenho de estirpes e formulamos uma taxonomia para os principais AOEs. Estes são avaliados em detalhe e as suas características principais são devidamente exploradas. Um dos problemas identificados prende-se com a sua oferta limitada de soluções de compromisso entre objetivos industriais (como produzir em excesso um composto de interesse, ou reduzir o custo de implementar uma solução) e objetivos celulares (como a maximização do crescimento). Para enfrentar este problema, desenvolvemos uma plataforma para otimização de estirpes baseada em computação evolucionária multiobjectivo, capaz de encontrar soluções de compromisso de elevada qualidade, que podem ser exploradas por peritos em engenharia metabólica. Para além disso, a grande maioria dos AOEs baseia-se em métodos de previsão de fenótipos que, por sua vez, são construídos sobre assunções biológicas discutíveis. Verificamos que uma grande percentagem das soluções geradas por um AOE, usando um método de previsão de fenótipos, deixaria de ser valida quando simulada com um método alternativo. Tirando partido da plataforma desenvolvida anteriormente e motivados pela natureza multiobjectivo deste problema, propusemos uma abordagem capaz de encontrar estirpes que respeitassem as assunções de diferentes métodos de previsão de fenótipos. Esta abordagem foi validada com vários casos de estudo. Por fim, todos os algoritmos desenvolvidos ao longo deste trabalho são disponibilizados sob a forma de uma aplicação de software aberto. Esta constitui uma ferramenta poderosa, tanto para utilizadores comuns interessados em explorar os métodos disponibilizados, como para programadores experientes que podem estendê-la facilmente com novos métodos.Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH/BD/61465/2009), co-financiada pelo POPH – QREN – Tipologia 4.1 – Formação Avançada – e comparticipado pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)