Genetic Programming meets Model-Driven Development

Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimize specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the utility of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach – the generation of source code in different programming languages.

Applicability of Emergence Engineering to Distributed Systems Scenarios

Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Colombia: compromise or containment?

The design, reformulation, and final signing of Plan Colombia by the then US President, Bill Clinton, on the 13 July 2000 initiated in a new era of the US State´s involvement in supposedly sovereign-territorial issues of Colombian politics. The implementation of Plan Colombia there-on-after brought about a major realignment of political-military scales and terrains of conflict that have renewed discourses concerning the contemporary imperialist interests of key US-based but transnationally-projected social forces, leading to arguments that stress the invigorated geo-political dimension of present-day strategies of capitalist accumulation. With the election of Álvaro Uribe Vélez as Colombian President in May 2002 and his pledge to strengthen the national military campaign aganist the region´s longest-surviving insurgency guerrilla group, Las FARC-EP, as well as other guerrilla factions, combined with a new focus on establishing the State project of “Democratic Security”; the military realm of governance and attempts to ensure property security and expanding capitalist investment have attained precedence in Colombia´s national political domains. This working paper examines the interrelated nature of Plan Colombia -as a binational and indeed regional security strategy- and Uribe´s Democratic Security project as a means of showing the manner in which they have worked to pave the way for the implementation of a new “total market” regime of accumulation, based on large-scale agro-industrial investment which is accelerated through processes of accumulation via dispossession. As such, the political and social reconfigurations involved manifest the multifarious scales of governance that become intertwined in incorporating neoliberalism in specific regions of the world economy. Furthermore, the militarisation-securitisation of such policies also illustrate the explicit contradictions of neoliberalism in a peripheral context, where coercion seems to prevail, something which leads to a profound questioning of the extent to which neoliberalism can be thought of as a hegemonic politico-economic project.