Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes

Quality-Diversity (QD) algorithms are a recent type of optimisation methods that search for a collection of both diverse and high performing solutions. They can be used to effectively explore a target problem according to features defined by the user. However, the field of QD still does not possess extensive methodologies and reference benchmarks to compare these algorithms. We propose a simple benchmark to compare the reliability of QD algorithms by optimising the Rastrigin function, an artificial landscape function often used to test global optimisation methods.Comment: 3 pages, 2 figure

Illuminating meaningful diversity in complex feature spaces through adaptive grid-based genetic algorithms

In many fields there exist problems for which multiple solutions of suitably high performance may be found across distinct regions of the search space. Optimisation of the search towards including these distinct solutions is important not only to understanding these spaces but also to avoiding local optima. This is the goal of a type of genetic algorithms called illumination algorithms. In Chapter 2, we demonstrate the use of an illumination algorithm in the exploration of networks sharing only a given set of structural features (valid networks). This method produces a population of valid networks that are more diverse than those produced using state of the art methods, however, it was found to be too inefficient to be usable in real-world problems. Additionally, setting an appropriate resolution of the search requires some amount of prior knowledge of the space of solutions. Addressing this problem is the focus of Chapter 3, in which we develop three extensions to the method: a) an exact method of mutation whereby only valid networks are explored, b) an adaptive mechanism for setting the resolution of the search, c) a principle for tuning mutations parameters to the search’ s resolution. We show that with these additions our method is able to increase the diversity of solutions found in significantly fewer iterations. Finally, in Chapter 4 we expand our method for use in more general problem spaces. We benchmark it against the state of the art. In all tested landscapes, we show that our method is able to identify more meaningful niches in the spaces in the same number of iterations. We conclude by highlighting the limits of our framework and discuss further directions

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Detecting the Scale and Resolution Effects in Remote Sensing and GIS.

This study examines the relationship between resolution and fractal dimensions of remotely sensed images. Based on the results of testing for the reliability of the algorithms on hypothetical surfaces, the isarithm algorithm is selected for determining the fractal dimensions of remotely sensed images. This algorithm is then applied to simulated fractal Brownian motion images and four calibrated airborne multispectral remotely sensed image data sets with different true and artificial resolutions for Puerto Rico. The results from applying the fractal method to images at different levels of resolution suggest that the higher the resolution of an image, the higher the fractal dimension of the image and the more complex the image surface. This relationship between resolution and fractal dimension is further verified by results from analysis employing the local variance method for the same data sets; where it is found that the higher the resolution, the higher the local variance or the more complex the image surface. The images with artificial resolutions were found to be unrealistic in simulating images with different resolutions because the aggregate method used in generating these images dose not exactly simulate the sensor\u27s response to resolution changes. The aggregate method has been widely used in image resampling and cautious use of this algorithm is suggested in future studies. The findings show that the fractal method is a useful tool in detecting the scale and resolution effects of remotely sensed images and in evaluating the trade-offs between data volume and data accuracy. More studies employing fractals and other spatial statistics to images with different artificial resolutions generated using better aggregation algorithms are needed in the future in order to further detect the scale and resolution effects in remote sensing and GIS

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

Computer vision and optimization methods applied to the measurements of in-plane deformations

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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

NEW ADVANCED TECHNOLOGIES FOR SURVEY AND ANALYSISbOF AGROFORESTRY LAND: FROM LAND COVER CHANGES TO RURAL LANDSCAPE QUALITY ASSESSMENT

The general objective of this Ph.D. thesis is to explore the concepts and methodologies for investigating agroforestry land and rural landscape through the integration of historical and remote sensing geodata within a FoSS (Free and Open Source Software) approach; to provide more and more accurate data sets regarding land cover and to improve some mapping and data processing techniques commonly used in this research topic. The first part of thesis describes the different types of geodata used in the course of the studies and, above all, the techniques and methodologies used for their processing are illustrated. Starting from historical cartographies, we will go through aerial surveys and geographical maps up to the new remote sensing using advanced satellite observation technologies. In the second part, more specific issues were dealt in accordance with the general objective of the work have been defined. The issues were approached through case studies within the Basilicata Region where the intensity of the abandonment of the territory and agricultural surface is leading to the loss of many historical rural landscapes and with consequent problems from an ecological point of view due to the disappearance of many agroforestry systems.L'obiettivo generale di questa tesi di dottorato è quello di esplorare i concetti e le metodologie per lo studio del territorio agroforestale e del paesaggio rurale attraverso l'integrazione di geodati storici e telerilevamento con un approccio FoSS (Free and Open Source Software); per fornire serie di dati sempre più accurate sulla copertura del suolo e migliorare alcune tecniche di mappatura ed elaborazione comunemente utilizzate in questo ambito di ricerca. La prima parte della tesi descrive i diversi tipi di geodati impiegati nel corso degli studi e, soprattutto, vengono illustrate le tecniche e le metodologie utilizzate per la loro elaborazione. Partendo dalle cartografie storiche, si passerà ai rilievi aerei ed alle cartogrofaie classifche fino al remote sensing basato su immagini satellitari. Nella seconda parte sono state trattate tematiche più specifiche in accordo con l'obiettivo generale del lavoro. Le tematiche sono state affrontate attraverso casi di studio all'interno della Regione Basilicata dove l'intensità dell'abbandono del territorio e della superficie agricola sta portando alla perdita di molti paesaggi rurali storici con conseguenti problemi dal punto di vista ecologico dovuti alla scomparsa di molti sistemi agroforestali