A general definition, which describes and characterizes a **dynamic** **fitness** function, is introduced here. The approach we follow assumes that each **dynamic** function consists of a base static function and a sequence of **dynamic** functions obtained from the base function and the application of a set of **dynamic** rules.

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A few authors have previously studied the influence of the migration poli- cies **in** stationary **environments**. Cantu-Paz [8] and Alba et al. [9] showed the benefits of sending a random individual instead of the best individual. Cur- rent multi-population approaches for DOPs have used migration policies. For instance, Oppacher and Wineberg **in** [5], send the elite (best) individuals from colonies subpopulations to a core subpopulation. Other policies used **in** litera- ture involve a global knowledge of the entire population, like Ursem **in** [6], by applying the hill-valley detection mechanism among the best individuals of each subpopulation, named nation. Recently, Park et al. [7] have used two populations with different **evolutionary** objectives and, given the inconvenience of normal mi- grations, they applied crossbreeding as a means of information exchange.

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Non–stationary, or **dynamic**, problems change over time. There exist a variety of forms of dynamism. The concept of **dynamic** **environments** **in** the context of this paper means that the **fitness** **landscape** changes during the run of an **evolutionary** algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity **in** the population. The algorithm was tested on a set of **dynamic** testing functions provided by a **dynamic** **fitness** problem generator. The main goal was to determinate the algorithm’s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability **in** the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.

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lumination within the EuRoC dataset [23] (we will refer to simulated sequences with an asterisk) we change the gain and bias of the image with two uniform distribution, i.e. α = U (0.5,2.5) and β = U(0, 20) pixels every 30 seconds. For that comparison, we not only focus **in** the accuracy of the estimated trajecto- ries, but also **in** the robustness of the algorithms under different environment conditions (we mark a dash those experiments where the algorithm lose the track). We compare the accuracy of trajectories obtained with our previous stereo VO system, PLVO [62], against our proposal tracking strategy, PLVO- L1, when employing LSD or FLD features. Table 4.5 contains the results by computing the relative RMSE **in** translation for the estimated trajectories. As we can observe, **in** the raw dataset our approach performs slightly worse than standard appearance-based tracking techniques, mainly due to the lower num- ber of correspondences provided by our algorithm, as mentioned **in** previous Section. **In** contrast, we can observe a considerable decrease **in** accuracy of our approaches, however, they are capable of estimating the motion **in** all se- quences with an lower accuracy, mainly due to the less number of matches, due to restrictive constraints. For this reason we believe our matching technique a suitable option to address the line segment tracking problem under severe ap- pearance changes, **in** combination with prior information from different sensors and/or algorithms.

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The superior performance of SRP-PHAT is well sup- ported by empirical evidence [4,8,9] yet there are just a few works aimed at giving formal explanations for its robustness. The authors of [24] evaluate SRP-PHAT and its variant β -PHAT so that they can emphasize the actual effect of the PHAT filtering, giving interesting insights about the effects of noise and reverberation **in** the localization per- formance. Unfortunately, the evaluation is purely experi- mental, without an analytic explanation, and based on simulated data. [13] shows that, under low noise and high reverberation conditions, SRP-PHAT is a special case of the maximum-likelihood estimator. Again, the results are based on simulated data and the assumptions made about the noise being gaussian do not hold when using real data [25]. The most recent known work **in** this area is described **in** [10]. Their approach is different to previous works, as they start from the signal models with some environmental assumptions, deriving an interesting analytic solution for the PHAT strategy. However, the formulation is only meant at explaining the PHAT robustness against reverberation, so that there is no attempt to further refine it by solving the frequency dependent terms, thus not allowing the deriva- tion of further considerations to be used **in** practical applications. Additionally, the validation of their proposal is again based on simulated data, thus compromising its possible application to real-world scenarios.

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The natural frequencies of the structure are computed and **in** good agreement with the values of the reference models and the **dynamic** equation of motion is integrated with the Newmark method, **in** particular the average acceleration method is used guar- anteeing unconditional stability. However, this work includes the rotation movement of the blades introducing a change **in** the geometry between time steps requiring the use of a non-linear integration algorithm although centrifugal stiffening and gyroscopic effects due rotation are neglected. **In** this case the non-linear Newmark integration method is utilized. The use of the non-linear integration method needs a reduction of the time step and a subsequent increase **in** the computational effort which is still manageable **in** terms of analysis but will shortly become an important issue **in** the following chapters. For fatigue analysis purposes the S-N approach of the current standards is applied using the Palgrem-Miner rule that considers the linearity of the accumulated damage. Also, fatigue is computed **in** the hot-spots of the tubular joints considering as well the SCFs. **In** terms of counting life-span stresses the rainflow counting algorithm is used for computations of 300 and 600 seconds to calculate the damage at those ages. The design life damage is linearly extrapolated from those two values for each hot-spot.

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The required data were captured using a Dell wireless LAN card utility analyzer/monitor (software). This card is controlled with special DELL driver installed on a client laptop which permits the collection of RF monitoring mode measurements. **In** this mode, the card is prohibited from associating itself with any access point (AP), but instead scans available Wireless Fidelity (WiFi) channels and display measurements of the RSS of the WiFi selected, indicating the RSS for each AP it samples. With this method, it is possible to measure all APs whose RSS is within the **dynamic** range of the card at any given point. The indoor environment operated at a frequency of 2.4GHz and its signal was transmitted via a Linksys Wireless-G access point (WRT54G) with 802.11g wireless network standard and compatible with 802.11b standard with maximum data transfer rate of 54Mbps. The outdoor signal transmitted using three sector beam forming antennas at 90 o , each facing different directions with connected amplifiers. Its transmitting frequency also operated at 2.4GHz. Both transmitting routers were set to infrastructure mode. The data were obtained over a distance of 10m and 100 m **in** steps of 1m and 10m respectively, from the access points **in** both **environments**. The reason for choosing same measurement lengths is that both case studies had same operating capacity and the necessity of obtaining balanced readings from both

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W h e n formalizing applicability conditions for our transformations we will be interested in annotated programs, in which information about run-time behaviour is collected at program [r]

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Brocade, an enterprise recognized **in** the area of Fibre Channel’ s switches, is at the moment one of the factories for Fabric topologies **in** Storage Area Networks (SAN). This company defines SAN as a network for storage and system components, which are all communicated **in** a Fibre Channel net, used to consolidate and share information, offering high performance links, high availability links, higher speed backups, and support for clustering servers.

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optimal material distribution within a given design space. For example, it takes out the elements under low stress **in** geometry by modifying the apparent material density, considered as a design variable **in** a FEM model. A basic FE model is created and analyzed **in** a design area with given boundary conditions. Commonly, the aims are to maximize stiffness or maximize the natural frequency of a product. The constraints of the design are the following: the fixations, material volume and largest displacement allowed. The design variables are the material density of the elements, which are counted commonly **in** hundreds of thousands, which means a huge number of design variables. The goal is, given a predefined design domain **in** the 2D/3D space with structural boundary conditions and load definitions, is to distribute a given mass, which is a given percentage of the initial mass **in** the domain determined, **in** such a way that a global measure takes a minimum (maximum) value. This type of topology variation is being analyzed only as a reference and basis for the kind of **optimization** that is going to be derived **in** this dissertation. Shape **Optimization** consists of changing the external borders of a mechanical part [22]. The geometry of the product is defined **in** terms of surfaces and curve parameters that define the outer boundary of the product, and allows more freedom for manipulation. Here, the topology remains unchanged. The shape of the structure is modified by the node locations of a product modeled with the finite element method (FEM). The aims are to decrease the stress or the volume or maximize the natural frequency. Constrains to the design include fixations and restrictions for displacement of part borders. The design variables of the product for geometric models are length, angle and radii measurements; and for the FE model, node coordinates. After defining a topology for a shape **optimization** problem, a common practice is to use a fixed set of shape variables to describe the design boundaries [23]. The values of the shape variables are then optimized to provide the lightest possible structural member. Specification of the initial set of shape variables is done while maintaining accurate structural analysis predictions by automating the variable selection process.

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Best parameter setting was determined by taking previously found results for this problem presented elsewhere [9]. For the experiments discussed here parameters were set as follows. Crossover and muta- tion probabilities fixed at 0.65 and 0.005, respectively. One of the main conclusions from the previous work is that the algorithm keeps evolving still **in** advanced generations, so a maximum number of gen- erations was fixed at 50000. The population size was fixed at 100 individuals. Elitism was used to re- tain the best individual found so far under each criterion. As optimal values of the makespan were known for each instance of the test suite, the common due date d to determine f 2 ( σ ) values was fixed at

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“We, the **landscape** architects, concerned with the future development of our landscapes **in** a fast changing world, believe that everything, influencing the way **in** which the outdoor environment is created, used, and maintained is fundamental to sustainable development and human well-being. We, being responsible for the improvement of the education of future **landscape** architects to enable them to work for a sustainable environment within the context of our natural and cultural heritage”. (IFLA/UNESCO 2005) CHARTER FOR **LANDSCAPE** ARCHITECTURAL EDUCATION)

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and it is an elementary **landscape** for the reduced reversals neighborhood. Proof. First, we need to show that all of the n− 1 edges that contributes to f (x) are uniformly broken by the reverse operator when all of the neighbors of f (x) are generated. The segments to be reversed range **in** length from 2 to n − 1. If the length of the segment is i, then the number of possible segments of length i is n − (i − 1). Let us consider reversals of length i and n − i + 1 together, where 1 < i ≤ n/2. The reversal of length i will break the first and last i − 1 edges **in** the permutation only once, but it will break all interior edges twice. However the reversal of length n − i + 1 will only break the first and last i − 1 edges, and it will break these edges only once. Thus, grouping these together, all edges are broken twice for each value of i.

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This work deals with finding new wavelets for specific type of images by means of **evolutionary** al- gorithms (EA), specifically using Evolution Strategies (ES) [4]. The intended deployment platform is an FPGA embedded system. Therefore, a relatively low computing power is available what will, undoubtedly, affect the performance of the **evolutionary** search, but provides the system with adaptation capabilities, so that image compression performance can be adjusted for the specific type of images the encoder is dealing with. Therefore, a hardware oriented algorithm has to be developed to assure the feasibility of the imple- mentation. For these reasons, issues such as the use of complex **evolutionary** operators and a fixed point implementation and validation of the algorithm have to be addressed.

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In ubiquitous data stream mining, the feature space of the records that occur in the data stream may change over time 15 or be different among devices 3 0.. For example, in a strea[r]

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The wave equation makes it possible to compute the av- erage value of the **fitness** function f evaluated over all of the neighbors of x using only the value f(x), that is, it provides a way of computing non-trivial statistics with a low com- putational cost. The previous average can be interpreted as the expected value of the objective function when a ran- dom neighbor of x is selected using a uniform distribution. This is exactly the behaviour of the so-called 1-bit-flip mu- tation [13]. It could seem that the restriction imposed by Grover’s wave equation cannot be frequently found **in** op- timization problems. However, there are some well-known NP-hard problems using common neighborhoods that are el- ementary landscapes. This is the case of the Not All Equals SAT problem, the Travelling Salesman Problem, the Graph Coloring problem, etc. The interested reader can find ex- amples of elementary landscapes **in** [25, 26].

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If new facts are carelessly added, Ψ may become inconsistent. To avoid this we have defined an updating process [6] that removes any element of Ψ contradicting the new observation. Note that according to our criterion, new perceptions are always preferred over older ones. There is a simple reason behind this policy: given our initial assumption, both of the observations **in** disagreement were correct at the time of their assimilation. As a result, the only explanation for the conflict is a change **in** the state of world, and the new fact should be favored since it reflects the actual state. It is worth mentioning that by updating the set of observations the agent can modify its beliefs, changing its previous picture of the world when faced with new information.

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The analysis of the approximated Pareto sets with five different quality indicators based on the corresponding noiseless Pareto fronts showed that the proposed solution ranking method [r]

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