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

Evangelista, Pedro

Gonçalves, Emanuel

Maia, Paulo

Pinho, Jorge

Rocha, Miguel

Sobral, João Luís Ferreira

Universidade do Minho: RepositoriUM

A Software Platform for EvolutionaryComputation with Pluggable Parallelism andQuality AssurancePedro Evangelista1,2, Jorge Pinho1, Emanuel Gonc¸alves1, Paulo Maia1,2, Joa˜oLuis Sobral1, and Miguel Rocha11 Department of Informatics / CCTC - University of Minhojls@di.uminho.pt mrocha@di.uminho.pt2 IBB - Institute for Biotechnology and BioengineeringCentre of Biological Engineering - University of MinhoCampus de Gualtar, 4710-057 Braga - PORTUGALptiago@deb.uminho.pt paulo.maia@deb.uminho.ptAbstract. This paper proposes the Java Evolutionary Computation Li-brary (JECoLi), an adaptable, flexible, extensible and reliable softwareframework implementing metaheuristic optimization algorithms, usingthe Java programming language. JECoLi aims to offer a solution suitedfor the integration of Evolutionary Computation (EC)-based approachesin larger applications, and for the rapid and efficient benchmarking ofEC algorithms in specific problems. Its main contributions are (i) the im-plementation of pluggable parallelization modules, independent from theEC algorithms, allowing the programs to adapt to the available hardwareresources in a transparent way, without changing the base code; (ii) aflexible platform for software quality assurance that allows creating testsfor the implemented features and for user-defined extensions. The libraryis freely available as an open-source project.Keywords: Evolutionary Computation, Open-source software, ParallelEvolutionary Algorithms, Software Quality1 IntroductionThe field of EC has been rapidly growing in the last decades, addressing complexoptimization problems in many scientific and technological areas. Also, there hasbeen the proposal of several software platforms with varying sets of function-alities, implemented in distinct programming languages and systems. Althoughthe authors acknowledge the existence of interesting platforms implementing ECapproaches, none of the ones tested was able to simultaneously respond to theset of requirements underlying our needs, namely: (i) robustness, modularity andquality assurance in the process of developing EC based components for othersystems and applications; (ii) efficiency in allowing the rapid benchmarking ofdistinct approaches in specific optimization tasks, mainly when taking advantageof the currently available parallel hardware.2 P. Evangelista et alThis paper introduces the Java Evolutionary Computation Library (JECoLi),a reliable, adaptable, flexible, extensible and modular software framework thatallows the implementation of metaheuristic optimization algorithms, using theJava programming language. The main contributions are its built-in capabilitiesto take full advantage of multicore, cluster and grid environments and the inclu-sion of a quality assurance platform, an added value in validating the software.In this work, we show how independent parallelism models and platformmappings can be attached to the basic framework to take advantage of newcomputing platforms. Our objective is to support parallel processing allowingthe user to specify the type of parallelization needed. The parallelization modelsare independent of the framework, and the user can specify the parallel executionmodel to meet specific computational methods and resources, taking advantageof the processing power of each target platform.On the other hand, the quality assurance framework developed aims at pro-viding a flexible way for both the developers of JECoLi and JECoLi-based appli-cations to be able to validate the available features such as algorithms, operators,parallelization features, etc. This framework should allow to conduct softwaretests with minimal programming.2 Main functionalitiesJECoLi already includes a large set of EC methods: general purpose Evolution-ary/ Genetic Algorithms, Simulated Annealing (SA), Differential Evolution, Ge-netic Programming and Linear GP, Grammatical Evolution, Cellular AutomataGAs and Multi-objective Evolutionary Algorithms (NSGA II, SPEA2). Amongthe parameters that can be configured, we find the encoding scheme, the repro-duction and selection operators, and the termination criteria.In JECoLi, solutions can be encoded using different representations. A gen-eral purpose representation using linear chromosomes is available including bi-nary, integer or real valued genes. Also, individuals can be encoded as permuta-tions, sets and trees. Numerous reproduction operators are provided for each ofthese representations. It is also possible to add new user-defined representationsand/ or reproduction operators.A loose coupling is provided between optimization algorithms and problemsallowing the easy integration with other software. New problems are addedby the definition of a single class specifying the evaluation function. An ade-quate support is also given to the development of hybrid approaches, such aslocal optimization enriched Evolutionary Algorithms (EAs) (e.g. Memetic Al-gorithms), hybrid crossover operators and initial population enrichment withproblem-specific heuristics.The framework is developed in Java, being portable to the major systems.The project is open source, released under the GPL license. Extensive docu-mentation (howto’s, examples and a complete API), binaries and source codereleases are available in the Wiki-based project’s web site http://darwin.di.uminho.pt/jecoli.A Software Platform for Evolutionary Computation 33 Overall architecture and development principlesFig. 1. Architecture of the JECoLi: main classes.The JECoLi architecture was built to be an adaptable, flexible, extensible,reliable and modular software platform. In Figure 1 a simplified class diagram isshown, containing the main entities. The Algorithm abstraction represents theoptimization method. The information common to all algorithms was abstractedin the AbstractConfiguration class. The algorithm dependent configuration de-tails are stored in the concrete implementation. When an algorithm starts exe-cuting, it checks all setup components, executes a specific initialization routine,and finally executes a series of iterations. During the execution, each algorithmholds an internal state that stores the current solution set, results and statisticscollected during the previous iterations. Several alternative termination criteriacan be defined (e.g. number of generations, function evaluations, CPU time).Classes representing individuals implement interface ISolution, including thegenome (with a specific representation) and the fitness value(s). Implementationof specific representations follow the IRepresentation interface. The solution fac-tory design pattern was used to allow building new solutions and copying existingones. These factories are a centralized location for restrictions applied to a spe-cific genome. Populations are implemented by the SolutionSet that implementslists of solutions with enhanced functionalities.An evaluation function is responsible for decoding an individual to a par-ticular problem domain and providing for fitness evaluation. This makes theconnection between the problem and the algorithm domains. One of the majoraims was the extensibility of the platform, implemented by defining contracts(using interfaces) for specific components enabling the addition of new algo-rithms, representations, operators, termination criteria, etc.4 P. Evangelista et al4 Parallelization of the libraryThe parallelization strategy is based on three key characteristics:– Non-invasive - parallelization should have minimal impact on the originalcode; using/ developing functionalities in the base JECoLi does not requirethe developer to have knowledge about the parallelism models/mappings;– Localized - parallelization code should be localized in well defined modules;– Pluggable - the original code can execute when parallelism modules areadded/ removed, i.e. parallelism modules are included on request.Thus, the parallelization of the library consists of (i) the base JECoLi; (ii) aset of non-invasive and pluggable modules that adapt the base framework to sup-port parallel execution and a set of mappings that adapt the supported modelsof parallel execution to each target platform; (iii) a tool that composes the baseframework with modules for parallel execution and platform mappings, accord-ing to the user requests. Two distinct conceptual models for the parallelizationof EAs are implemented: (i) the island model; and (ii) the parallel solution eval-uation. These models do not imply any specific parallel environment and can beexecuted even in sequential architectures.Fig. 2. Architecture of the Parallelization of JECoLi.The parallelization of JECoLi is based on a three-layer architecture depictedin Figure 2. The features were made based on layers: the first layer is the orig-inal JECoLi; the second contains the parallelism models to use, and the lastone introduces the mapping of the parallel behavior to the execution platform.The parallelism model layer contains the parallel models presented above: islandmodel and parallel execution of the evaluation. Three distinct modules were im-plemented in this layer: Parallel Eval, BuildIslandModel and Abstract Migration,implementing the parallelization features that encapsulate platform independentbehaviour. The first implements the model related to the parallel execution ofthe evaluation of solutions, while the second and third are related to the imple-mentation of the island model. The mappings layer contains all the specific codeto be loaded regarding a specific parallel platform. It encapsulates the mappingof the parallelism models to meet specific target parallel environments: singlemachine with multicore processor, clusters and grid environments.A Software Platform for Evolutionary Computation 5The implementation of the modules resorted to the use of Aspect OrientedProgramming (AOP) [2], a programming technique used to encapsulate crosscut-ting phenomenona into specific modules of the programs. Technical details of thefull implementation of the modules are provided in the web site documentation.5 Quality assurance frameworkA generic test framework was engineered in order to validate JECoLi’s function-alities. The main aim is to verify the correctness of the distinct components. Thetests treat each component of the library as a black-box entity with a predefinedset of inputs and a desired output. These are defined in order to cover all thepossible outcomes of an entity for a certain input even if the library componentonly returns a partial result set. If the outcome of an entity is contained withinthe full result set the test is considered valid.A domain specific language (DSL) with an LL(1) grammar was conceivedto create JUnit tests [1] for the distinct components (algorithms, operators andparallelization methods). This language captures the tests configuration skeleton.Examples of configurations of specific tests are provided in the project web site.Also, a test framework was developed encompassing a class hierarchy thatis given in the web site documentation. The implementation of the abstractclasses in the lower level of the class hierarchy only needs to detail componentconfiguration information. These structures allow to capture seamlessly the teststructure and to implement it in a domain specific language without the needto program in Java to add new tests for existing components.6 ApplicationsThe library has already been used in several research projects, described inthe web site. Here, we highlight a few of these examples that have led to thedevelopment of software applications:– Metabolic engineering: the aim is microbial strain optimization, using insilico simulations based on genome-scale metabolic models. EAs and SAusing a set-based representation have been used [5] and implemented as partof the OptFlux software platform (www.optflux.org) [4], that represents areference open-source software platform.– Fermentation optimization: aims at the numerical optimization of feedingprofiles in fed-batch fermentation processes in bioreactors [3]. EAs with areal value representations and several variants of DE were tested and eval-uated. The implementation of these approaches was included in a softwareapplication named OptFerm (darwin.di.uminho.pt/optferm).– Network traffic engineering: the aim is to implement methods to improvethe weights of intra-domain routing protocols [6]. Two approaches were used,both using an integer representation: single objective EAs with linear weight-ing objective functions and multiobjective EAs. These methods have beenincorporated into an application - NetOpt (darwin.di.uminho.pt/netopt).6 P. Evangelista et al7 ConclusionsThis paper described the framework JECoLi, a Java-based platform for the im-plementation of metaheuristic methods. The main strengths of this platform relyon its efficiency, portability, flexibility, modularity and extensibility which makeit ideal to support larger applications that need to include an optimization en-gine based on EC methods. The main focus of this library has been to supportthis embedding capabilities also by making available a quality assurance frame-work, including a domain specific language for building unit tests, that allowsthe full coverage of the implemented features.In terms of computational efficiency, one of the major concerns has been thedevelopment of modules to support parallelism. The parallelization of JECoLiwas pursued in a pluggable, non-invasive and localized way, thus allowing theoriginal library to evolve with new features being added and also to allow theautomatic adaptation of the code to distinct scenarios in terms of hardwareresources (multicore, cluster, grid).Most of the future work includes the improvement of the existing function-alities. Also, we aim to develop new capabilities including new algorithms, rep-resentations or selection/reproduction operators.AcknowledgmentsThis work is supported by project PTDC/EIA-EIA/115176/2009, funded byPortuguese FCT and Programa COMPETE.References1. Y. Cheon and G. Leavens. A simple and practical approach to unit testing: TheJML and JUnit way. ECOOP 2002, Object-Oriented Programming, pages 1789–1901, 2006.2. G.J. Kiczales, J.O. Lamping, C.V. Lopes, J.J. Hugunin, E.A. Hilsdale, and C. Boy-apati. Aspect-oriented programming, October 15 2002. US Patent 6,467,086.3. R. Mendes, I. Rocha, E. Ferreira, and M. Rocha. A comparison of algorithms forthe optimization of fermentation processes. In 2006 IEEE Congress on EvolutionaryComputation, pages 7371–7378, Vancouver, BC, Canada, jul 2006.4. I. Rocha, P. Maia, P. Evangelista, P. Vilac¸a, S. Soares, J. P. Pinto, J. Nielsen, K.R.Patil, E.C. Ferreira, and M. Rocha. Optflux: an open-source software platform forin silico metabolic engineering. BMC Systems Biology, 4(45), 2010.5. M. Rocha, P. Maia, R. Mendes, E.C. Ferreira, K. Patil, J. Nielsen, and I. Rocha. Nat-ural computation meta-heuristics for the in silico optimization of microbial strains.BMC Bioinformatics, 9(499), 2008.6. P. Sousa, M. Rocha, M. Rio, and P. Cortez. Efficient OSPF Weight Allocation forIntra-domain QoS Optimization. In Lecture Notes in Computer Science 4268, 16.Autonomic Principles of IP Operations and Management, pages 37–48. Springer,2006.

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

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

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