Modification of species-based differential evolution for multimodal optimization

At this time optimization has an important role in various fields as well as between other operational research, industry, finance and management. Optimization problem is the problem of maximizing or minimizing a function of one variable or many variables, which include unimodal and multimodal functions. Differential Evolution (DE), is a random search technique using vectors as an alternative solution in the search for the optimum. To localize all local maximum and minimum on multimodal function, this function can be divided into several domain of fitness using niching method. Species-based niching method is one of method that build sub-populations or species in the domain functions. This paper describes the modification of species-based previously to reduce the computational complexity and run more efficiently. The results of the test functions show species-based modifications able to locate all the local optima in once run the program

An Image fusion algorithm for spatially enhancing spectral mixture maps

An image fusion algorithm, based upon spectral mixture analysis, is presented. The algorithm combines low spatial resolution multi/hyperspectral data with high spatial resolution sharpening image(s) to create high resolution material maps. Spectral (un)mixing estimates the percentage of each material (called endmembers) within each low resolution pixel. The outputs of unmixing are endmember fraction images (material maps) at the spatial resolution of the multispectral system. This research includes developing an improved unmixing algorithm based upon stepwise regression. In the second stage of the process, the unmixing solution is sharpened with data from another sensor to generate high resolution material maps. Sharpening is implemented as a nonlinear optimization using the same type of model as unmixing. Quantifiable results are obtained through the use of synthetically generated imagery. Without synthetic images, a large amount of ground truth would be required in order to measure the accuracy of the material maps. Multiple band sharpening is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. The analysis includes an examination of the effects of constraints and texture variation on the material maps. The results show stepwise unmixing is an improvement over traditional unmixing algorithms. The results also indicate sharpening improves the material maps. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map

Modes of Interaction in Computational Architecture

This thesis is an enquiry into the importance and influence of interaction in architecture, the importance of which is observed through different modes of interaction occurring in various aspects of architectural discourse and practice. Interaction is primarily observed through the different use of software within architectural practice and in the construction of buildings, façades and systems. In turn, the kind of influences software has on architecture is one of the underlying questions of this thesis. Four qualities: Concept, Materiality, Digitization and Interactivity, are proposed as a theoretical base for the analysis and assessment of different aspects of computational architecture. These four qualities permeate and connect the diverse areas of research discussed, including architecture, cybernetics, computer science, interaction design and new media studies, which in combination provide the theoretical background. The modalities of computational architecture analysed here are, digital interior spaces, digitized design processes and communicational exterior environments. The analysis is conducted through case studies: The Fun Palace, Generator Project, Water Pavilion, Tower of Winds, Institute du Monde Arabe, The KPN building, Aegis Hyposurface, BIX Façade, Galleria Department Store, Dexia Tower, and also E:cue, Microstation, Auto-Cad, Rhino, Top Solid and GenerativeComponents software. These are important for discussion because they present different architectural concepts and thoughts about interactivity within architecture. The analytical processes used in the research distinguished and refined, eight modes of interaction: (1) interaction as a participatory process; (2) cybernetic mutualism; (3) thematic interaction; (4) human-computer interaction during architectural design production; (5) interaction during digital fabrication; (6) parametric interaction; (7) kinetic interaction with dynamic architectural forms; and (8) interaction with façades. Out of these, cybernetic mutualism is the mode of interaction proposed by this thesis

Adaptive evolution in static and dynamic environments

This thesis provides a framework for describing a canonical evolutionary system. Populations of individuals are envisaged as traversing a search space structured by genetic and developmental operators under the influence of selection. Selection acts on individuals' phenotypic expressions, guiding the population over an evaluation landscape, which describes an idealised evaluation surface over the phenotypic space. The corresponding valuation landscape describes evaluations over the genotypic space and may be transformed by within generation adaptive (learning) or maladaptive (fault induction) local search. Populations subjected to particular genetic and selection operators are claimed to evolve towards a region of the valuation landscape with a characteristic local ruggedness, as given by the runtime operator correlation coefficient. This corresponds to the view of evolution discovering an evolutionarily stable population, or quasi-species, held in a state of dynamic equilibrium by the operator set and evaluation function. This is demonstrated by genetic algorithm experiments using the NK landscapes and a novel, evolvable evaluation function, The Tower of Babel. In fluctuating environments of varying temporal ruggedness, different operator sets are correspondingly more or less adapted. Quantitative genetics analyses of populations in sinusoidally fluctuating conditions are shown to describe certain well known electronic filters. This observation suggests the notion of Evolutionary Signal Processing. Genetic algorithm experiments in which a population tracks a sinusoidally fluctuating optimum support this view. Using a self-adaptive mutation rate, it is possible to tune the evolutionary filter to the environmental frequency. For a time varying frequency, the mutation rate reacts accordingly. With local search, the valuation landscape is transformed through temporal smoothing. By coevolving modifier genes for individual learning and the rate at which the benefits may be directly transmitted to the next generation, the relative adaptedness of individual learning and cultural inheritance according to the rate of environmental change is demonstrated

A probabilistic cooperative-competitive hierarchical search model.

by Wong Yin Bun, Terence.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 99-104).Abstract also in Chinese.List of Figures --- p.ixList of Tables --- p.xiChapter I --- Preliminary --- p.1Chapter 1 --- Introduction --- p.2Chapter 1.1 --- Thesis themes --- p.4Chapter 1.1.1 --- Dynamical view of landscape --- p.4Chapter 1.1.2 --- Bottom-up self-feedback algorithm with memory --- p.4Chapter 1.1.3 --- Cooperation and competition --- p.5Chapter 1.1.4 --- Contributions to genetic algorithms --- p.5Chapter 1.2 --- Thesis outline --- p.5Chapter 1.3 --- Contribution at a glance --- p.6Chapter 1.3.1 --- Problem --- p.6Chapter 1.3.2 --- Approach --- p.7Chapter 1.3.3 --- Contributions --- p.7Chapter 2 --- Background --- p.8Chapter 2.1 --- Iterative stochastic searching algorithms --- p.8Chapter 2.1.1 --- The algorithm --- p.8Chapter 2.1.2 --- Stochasticity --- p.10Chapter 2.2 --- Fitness landscapes and its relation to neighborhood --- p.12Chapter 2.2.1 --- Direct searching --- p.12Chapter 2.2.2 --- Exploration and exploitation --- p.12Chapter 2.2.3 --- Fitness landscapes --- p.13Chapter 2.2.4 --- Neighborhood --- p.16Chapter 2.3 --- Species formation methods --- p.17Chapter 2.3.1 --- Crowding methods --- p.17Chapter 2.3.2 --- Deterministic crowding --- p.18Chapter 2.3.3 --- Sharing method --- p.18Chapter 2.3.4 --- Dynamic niching --- p.19Chapter 2.4 --- Summary --- p.21Chapter II --- Probabilistic Binary Hierarchical Search --- p.22Chapter 3 --- The basic algorithm --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Search space reduction with binary hierarchy --- p.25Chapter 3.3 --- Search space modeling --- p.26Chapter 3.4 --- The information processing cycle --- p.29Chapter 3.4.1 --- Local searching agents --- p.29Chapter 3.4.2 --- Global environment --- p.30Chapter 3.4.3 --- Cooperative refinement and feedback --- p.33Chapter 3.5 --- Enhancement features --- p.34Chapter 3.5.1 --- Fitness scaling --- p.34Chapter 3.5.2 --- Elitism --- p.35Chapter 3.6 --- Illustration of the algorithm behavior --- p.36Chapter 3.6.1 --- Test problem --- p.36Chapter 3.6.2 --- Performance study --- p.38Chapter 3.6.3 --- Benchmark tests --- p.45Chapter 3.7 --- Discussion and analysis --- p.45Chapter 3.7.1 --- Hierarchy of partitions --- p.45Chapter 3.7.2 --- Availability of global information --- p.47Chapter 3.7.3 --- Adaptation --- p.47Chapter 3.8 --- Summary --- p.48Chapter III --- Cooperation and Competition --- p.50Chapter 4 --- High-dimensionality --- p.51Chapter 4.1 --- Introduction --- p.51Chapter 4.1.1 --- The challenge of high-dimensionality --- p.51Chapter 4.1.2 --- Cooperation - A solution to high-dimensionality --- p.52Chapter 4.2 --- Probabilistic Cooperative Binary Hierarchical Search --- p.52Chapter 4.2.1 --- Decoupling --- p.52Chapter 4.2.2 --- Cooperative fitness --- p.53Chapter 4.2.3 --- The cooperative model --- p.54Chapter 4.3 --- Empirical performance study --- p.56Chapter 4.3.1 --- pBHS versus pcBHS --- p.56Chapter 4.3.2 --- Scaling behavior of pcBHS --- p.60Chapter 4.3.3 --- Benchmark test --- p.62Chapter 4.4 --- Summary --- p.63Chapter 5 --- Deception --- p.65Chapter 5.1 --- Introduction --- p.65Chapter 5.1.1 --- The challenge of deceptiveness --- p.65Chapter 5.1.2 --- Competition: A solution to deception --- p.67Chapter 5.2 --- Probabilistic cooperative-competitive binary hierarchical search --- p.67Chapter 5.2.1 --- Overview --- p.68Chapter 5.2.2 --- The cooperative-competitive model --- p.68Chapter 5.3 --- Empirical performance study --- p.70Chapter 5.3.1 --- Goldberg's deceptive function --- p.70Chapter 5.3.2 --- "Shekel family - S5, S7, and S10" --- p.73Chapter 5.4 --- Summary --- p.74Chapter IV --- Finale --- p.78Chapter 6 --- A new genetic operator --- p.79Chapter 6.1 --- Introduction --- p.79Chapter 6.2 --- Variants of the integration --- p.80Chapter 6.2.1 --- Fixed-fraction-of-all --- p.83Chapter 6.2.2 --- Fixed-fraction-of-best --- p.83Chapter 6.2.3 --- Best-from-both --- p.84Chapter 6.3 --- Empricial performance study --- p.84Chapter 6.4 --- Summary --- p.88Chapter 7 --- Conclusion and Future work --- p.89Chapter A --- The pBHS Algorithm --- p.91Chapter A.1 --- Overview --- p.91Chapter A.2 --- Details --- p.91Chapter B --- Test problems --- p.96Bibliography --- p.9

Third International Conference on Inverse Design Concepts and Optimization in Engineering Sciences (ICIDES-3)

Papers from the Third International Conference on Inverse Design Concepts and Optimization in Engineering Sciences (ICIDES) are presented. The papers discuss current research in the general field of inverse, semi-inverse, and direct design and optimization in engineering sciences. The rapid growth of this relatively new field is due to the availability of faster and larger computing machines

Geophysical modeling for groundwater and soil contamination risk assessment

This PhD thesis is focused on the study of environmental problems linked to contaminant detection and transport in soil and groundwater. The research has two main objectives: development, testing and application of geophysical data inversion methods for identifying and characterizing possible anomaly sources of contamination and development and application of numerical models for simulating contaminant propagation in saturated and unsaturated conditions. Initially, three different approaches for self-potential (SP) data inversion, based on spectral, tomographical and global optimization methods, respectively, are proposed to characterize the SP anomalous sources and to study their time evolution. The developed approaches are first tested on synthetic SP data generated by simple polarized structures, (like sphere, vertical cylinder, horizontal cylinder and inclined sheet) and, then, applied to SP field data taken from literature. In particular, the comparison of the results with those coming from other numerical approaches strengthens their usefulness. As it concerns the modelling of groundwater flow and contaminant transport, two cellular automata (CA) models have been developed to simulate diffusion-dispersion processes in unsaturated and saturated conditions, respectively, and to delineate the most dangerous scenarios in terms of maximum distances travelled by the contaminant. The developed CA models have been applied to two study areas affected by a different phenomenon of contamination. The first area is located in the western basin of the Crete island (Greece), which is affected by organic contaminant due to olive oil mills wastes (OOMWs). The numerical simulations provided by the CA model predict contaminant infiltration in the saturated zone and such results are in very good agreement with the high phenol concentrations provided by geochemical analyses on soil samples collected in the survey area at different depths and times. The second case study refers to an area located in the western basin of Solofrana river valley (southern Italy), which is often affected by heavy flooding and contamination from agricultural and industrial activities in the surroundings. The application of a multidisciplinary approach, which integrates geophysical data with hydrogeological and geochemical studies, and the development of a CA model for contaminant propagation in saturated conditions, have permitted to identify a possible phenomenon of contamination and the delineation of the most dangerous scenarios in terms of infiltration rates are currently in progress

Combined optimization algorithms applied to pattern classification

Accurate classification by minimizing the error on test samples is the main goal in pattern classification. Combinatorial optimization is a well-known method for solving minimization problems, however, only a few examples of classifiers axe described in the literature where combinatorial optimization is used in pattern classification. Recently, there has been a growing interest in combining classifiers and improving the consensus of results for a greater accuracy. In the light of the "No Ree Lunch Theorems", we analyse the combination of simulated annealing, a powerful combinatorial optimization method that produces high quality results, with the classical perceptron algorithm. This combination is called LSA machine. Our analysis aims at finding paradigms for problem-dependent parameter settings that ensure high classifica, tion results. Our computational experiments on a large number of benchmark problems lead to results that either outperform or axe at least competitive to results published in the literature. Apart from paxameter settings, our analysis focuses on a difficult problem in computation theory, namely the network complexity problem. The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. In order to investigate this problem, we introduce a new recursive learning method for training hidden layers in constant depth circuits. Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing an upper bound for the number of hidden units sufficient to achieve a high classification rate. One of the major findings of our research is that the size of the network can be bounded by the input size of the problem and an approximate upper bound of 8 + √2n/n threshold gates as being sufficient for a small error rate, where n := log/SL and SL is the training set

Architectural artificial intelligence: exploring and developing strategies, tools, and pedagogies toward the integration of deep learning in the architectural profession

The growing incessance for data collection is a trend born from the basic promise of data: “save everything you can, and someday you’ll be able to figure out some use for it all” (Schneier 2016, p. 40). However, this has manifested as a plague of information overload, where “it would simply be impossible for humans to deal with all of this data” (Davenport 2014, p. 151). Especially within the field of architecture, where designers are tasked with leveraging all available sources of information to compose an informed solution. Too often, “the average designer scans whatever information [they] happen on, […] and introduces this randomly selected information into forms otherwise dreamt up in the artist’s studio of mind” (Alexander 1964, p. 4). As data accumulates— less so the “oil”, and more the “exhaust of the information age” (Schneier 2016, p. 20)—we are rapidly approaching a point where even the programmers enlisted to automate are inadequate. Yet, as the size of data warehouses increases, so too does the available computational power and the invention of clever algorithms to negotiate it. Deep learning is an exemplar. A subset of artificial intelligence, deep learning is a collection of algorithms inspired by the brain, capable of automated self-improvement, or “learning”, through observations of large quantities of data. In recent years, the rise in computational power and the access to these immense databases have fostered the proliferation of deep learning to almost all fields of endeavour. The application of deep learning in architecture not only has the potential to resolve the issue of rising complexity, but introduce a plethora of new tools at the architect’s disposal, such as computer vision, natural language processing, and recommendation systems. Already, we are starting to see its impact on the field of architecture. Which raises the following questions: what is the current state of deep learning adoption in architecture, how can one better facilitate its integration, and what are the implications for doing so? This research aims to answer those questions through an exploration of strategies, tools, and pedagogies for the integration of deep learning in the architectural profession