1,134 research outputs found

    Open-ended Search through Minimal Criterion Coevolution

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    Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

    Understanding evolutionary processes during past Quaternary climatic cycles: Can it be applied to the future?

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    Climate change affected ecological community make-up during the Quaternary which was probably both the cause of, and was caused by, evolutionary processes such as species evolution, adaptation and extinction of species and populations

    Evolutionary improvement of programs

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    Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

    Coevolution in Complex Networks : An analysis of socio-natural interactions for wetlands management

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    Coevolution between natural and social systems comprises interaction, reciprocal dynamics and reciprocal adaptation. The notion derives primarily from evolutionary biology, but also from the study of complex systems. This dissertation aims to: “develop the means to assess the effects of different human interventions on the future coevolution of interacting natural and social systems.” The method that I develop is termed ‘topological network analysis’, highlighting my focus on the topology – number and pattern of interactions – of complex networks. A socio-natural network integrates interactions within and between a natural and a social system. Topological network analysis simulates and compares the effect of different human interventions on the network’s topology. It comprises four steps: 1. construction of a reference socio-natural network capturing the current situation for a given region; 2. specification of alternative development paths for the region; 3. translation of these paths into change in the network; and 4. comparison of the alternative paths according to their estimate impacts on the robustness of the network and so the stability of the system . This last step leads to management insights. Topological network analysis is illustrated by considering conversion of a stand of mangroves in the Philippines. The dissertation focuses on human intervention into ecosystem and on the potential for subsequent biodiversity loss. Topological network may be best applied to decision problems or management issues involving differential effects on species’ survival.Nijkamp, P. [Promotor]Opschoor, J.B. [Promotor

    The nature of culture : an eight-grade model for the evolution and expansion of cultural capacities in hominins and other animals

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    Tracing the evolution of human culture through time is arguably one of the most controversial and complex scholarly endeavors, and a broad evolutionary analysis of how symbolic, linguistic, and cultural capacities emerged and developed in our species is lacking. Here we present a model that, in broad terms, aims to explain the evolution and portray the expansion of human cultural capacities (the EECC model), that can be used as a point of departure for further multidisciplinary discussion and more detailed investigation. The EECC model is designed to be flexible, and can be refined to accommodate future archaeological

    Evolutionary multi-objective optimization in uncertain environments

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    Ph.DDOCTOR OF PHILOSOPH

    Data platforms for open life sciences-A systematic analysis of management instruments

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    Open data platforms are interfaces between data demand of and supply from their users. Yet, data platform providers frequently struggle to aggregate data to suit their users' needs and to establish a high intensity of data exchange in a collaborative environment. Here, using open life science data platforms as an example for a diverse data structure, we systematically categorize these platforms based on their technology intermediation and the range of domains they cover to derive general and specific success factors for their management instruments. Our qualitative content analysis is based on 39 in-depth interviews with experts employed by data platforms and external stakeholders. We thus complement peer initiatives which focus solely on data quality, by additionally highlighting the data platforms' role to enable data utilization for innovative output. Based on our analysis, we propose a clearly structured and detailed guideline for seven management instruments. This guideline helps to establish and operationalize data platforms and to best exploit the data provided. Our findings support further exploitation of the open innovation potential in the life sciences and beyond

    Multi objective particle swarm optimization: algorithms and applications

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    Ph.DDOCTOR OF PHILOSOPH
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