6,031 research outputs found
Studying Parallel Evolutionary Algorithms: The cellular Programming Case
Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile—though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO
This paper gives a concise overview of evolutionary algorithms for
multiobjective optimization. A substantial number of evolutionary computation
methods for multiobjective problem solving has been proposed so far, and an
attempt of unifying existing approaches is here presented. Based on a
fine-grained decomposition and following the main issues of fitness assignment,
diversity preservation and elitism, a conceptual global model is proposed and
is validated by regarding a number of state-of-the-art algorithms as simple
variants of the same structure. The presented model is then incorporated into a
general-purpose software framework dedicated to the design and the
implementation of evolutionary multiobjective optimization techniques:
ParadisEO-MOEO. This package has proven its validity and flexibility by
enabling the resolution of many real-world and hard multiobjective optimization
problems
ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization
This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
A genetic and evolutionary programming environment with spatially structured populations and built-in parallelism
The recent development of the Genetic and Evolutionary Computation field lead to a kaleidoscope of approaches to problem solving, which are based on a common background. These shared principles are used in order to develop a programming environment that enhances modularity, in terms of software design and implementation. The system's core encapsulates the main features of the Genetic and Evolutionary Algorithms, by identifying the entities at stake and implementing them as hierarchies of software modules. This architecture is enriched with the parallelization of the algorithms, based on spatially structured populations, following coarse-grained (Island Model) and fine-grained (Neighborhood Model) strategies. A distributed physical implementation, under the PVM environment, running in a local network, is described.Fundação para a Ciência e Tecnologia - PRAXIS/P/EEI/13096/98
Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review
Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP
Multimethod optimization in the cloud: A case‐study in systems biology modelling
[Abstract] Optimization problems appear in many different applications in science and engineering. A large number of different algorithms have been proposed for solving them; however, there is no unique general optimization method that performs efficiently across a diverse set of problems. Thus, a multimethod optimization, in which different algorithms cooperate to outperform the results obtained by any of them in isolation, is a very appealing alternative. Besides, as real‐life optimization problems are becoming more and more challenging, the use of HPC techniques to implement these algorithms represents an effective strategy to speed up the time‐to‐solution. In addition, a parallel multimethod approach can benefit from the effortless access to q large number of distributed resources facilitated by cloud computing. In this paper, we propose a self‐adaptive cooperative parallel multimethod for global optimization. This proposal aims to perform a thorough exploration of the solution space by means of multiple concurrent executions of a broad range of search strategies. For its evaluation, we consider an extremely challenging case‐study from the field of computational systems biology. We also assess the performance of the proposal on a public cloud, demonstrating both the potential of the multimethod approach and the opportunity that the cloud provides for these problems.Gobierno de España; DPI2014‐55276‐C5‐2‐RGobierno de España; DPI2017‐82896‐C2‐2‐RGobierno de España; TIN2016‐75845‐PXunta de Galicia; R2016/045Xunta de Galicia; ED431C 2017/0
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