The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a low percentage of women who suffer it can be detected using...
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ISBN:
(纸本)9728865422
The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a low percentage of women who suffer it can be detected using mammography methods. Therefore, it is necessary to develop new strategies to detect its formation in early stages. Many machine learning techniques have been applied in order to help doctors in the diagnosis decision process, but its definition and application are complex, getting results which are not often the desired. In this article we present an automatic way to build decision support systems by means of the combination of several machine learning techniques using a Meta-learning approach based on Grammar Evolution (MGE). We will study its application over different mammographic datasets to assess the improvement of the results.
This paper focuses on the management of recovered thermal energy of a hybrid wind energy and grid-parallel PEM fuel cell power plant (FCPP) with the object of achieving optimal cost. The fluctuating nature or wind ene...
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ISBN:
(纸本)9781424438105
This paper focuses on the management of recovered thermal energy of a hybrid wind energy and grid-parallel PEM fuel cell power plant (FCPP) with the object of achieving optimal cost. The fluctuating nature or wind energy (WE) has a different effect on the system operational cost and constraints. Besides, FCPPs are capable of producing both electrical and thermal energy. Combining WE and FCPP in a hybrid structure for CHP system yields lower operational cost than that of individual units. An economic approach is presented which includes the operational cost, thermal recovery, power trade with the local grid, and selling of surplus thermal energy. Multiple operational strategies are developed using this approach. The strategies are then evaluated by estimating the hourly generated power, the amount of recovered thermal energy while satisfying the thermal and electrical load requirements. An evolutionary programming-based technique is used to solve for the optimal operational strategy. Results are encouraging and indicate viability of the proposed approach.
As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms....
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ISBN:
(数字)9781522563204
ISBN:
(纸本)9781522563198;1522563199
As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms. Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.
Flexible Manufacturing Systems-FMS is a term with various types of definitions,each of them trying to describe the complexity and the generalized *** of these features is their complexity,along with difficulties in bu...
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ISBN:
(纸本)9783037856017
Flexible Manufacturing Systems-FMS is a term with various types of definitions,each of them trying to describe the complexity and the generalized *** of these features is their complexity,along with difficulties in building models that capture the system in all its important *** a heterogeneous flexible system,the scheduling events or actions could be a combinatorial problem which claims a particular *** scheduling process,in special for FMS,is a very difficult scheduling problem,because involves all the aspects of the processes:order,resources,transportation system *** vehicle guided,perturbation factors such as breakdowns of machine,***,the scheduling problem is a NP-hard problem modeled in mathematical *** we simulate n jobs or orders which have to be assigned to the m machines or resources,we will observe that the mathematical solution is a huge number that means(n!)m possibilities of *** challenge of researchers is to solve this equation in a reasonable time with an optimal solution,and of course with minimal *** scientists applied many solutions which became Operational Research-OR or Combinatorial Optimization-CO areas using a various methods:Local Search-LS,Artificial Intelligence-AI,heuristic method,priority rules,memetic or hybrid techniques which combine this techniques.
In this paper, we describe TOGAPS, a Testability-Oriented Genetic Algorithm for Pipeline Synthesis. The input to TOGAPS is an unscheduled data flow graph along with a specification of the desired pipeline latency. TOG...
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In this paper, we describe TOGAPS, a Testability-Oriented Genetic Algorithm for Pipeline Synthesis. The input to TOGAPS is an unscheduled data flow graph along with a specification of the desired pipeline latency. TOGAPS generates a register-level description of a datapath which is near-optimal. in terms of area, meets the latency requirement, and is highly testable, Genetic search is employed to explore a 3-D search space, the three dimensions being the chip area, average latency, and the testability of the datapath. Testability of a design is evaluated by counting the number of self-loops in the structure graph of the data path. Each design is characterized by a four-tuple consisting of (i) the latency and schedule information, (ii) the module allocation, (iii) operation-to-module binding, and (iv) value-to-register binding. Accordingly, we maintain the population of designs in a hierarchical manner. The topmost level of this hierarchy consists of the latency and schedule information, which together characterize the timing performance of the design. The middle level of the hierarchy consists of a number of allocations for a given latency/schedule duplet. The lowest level of the hierarchy consists of a number of bindings for a specific latency/schedule/allocation. An initial population of designs is constructed from the given data flow graph using different latency cycles whose average latency is in the specified range. Multiple scheduling heuristics are used to generate schedules for the DFG. For each of the resulting scheduled data flow graphs, we decide on an allocation of modules and registers based on a lower bound estimated using the schedule and latency information. The operation-to-module binding and the value-to-register binding are then carried out. A fitness measure is evaluated for each of the resulting data paths;this fitness measure includes one component for each of the three search dimensions. Crossover and mutation operators are used to generate ne
Evoluční techniky jsou neustále se vyvíjející a progresivní část informatiky. Evoluční algoritmy se v praxi používají k řešení mnohých druhů problémů od...
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Evoluční techniky jsou neustále se vyvíjející a progresivní část informatiky. Evoluční algoritmy se v praxi používají k řešení mnohých druhů problémů od optimalizace až k plánování. Tato práce se zabývá genetickým a kartézským genetickým programováním, které patří mezi nejčastěji používané algoritmy. Cílem práce je implementovat jednotlivé přístupy a vyhodnotit jejich účinnost v úloze symbolické regrese.
Diplomová práce se zabývá použitím zvolených evolučních algoritmů k určení a úpravě parametrů neuronové sítě. K úpravě parametrů sítě se zpětný...
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Diplomová práce se zabývá použitím zvolených evolučních algoritmů k určení a úpravě parametrů neuronové sítě. K úpravě parametrů sítě se zpětným šířením chyby byly použity genetické algoritmy, evoluční strategie a evoluční programování. Součástí práce je program vytvořený v prostředí Matlab, ve kterém byly použité metody testovány na úlohách rozpoznávání vzorů a predikci průběhu funkce. Výsledkem práce jsou grafy průběhu chyby sítě a fitness během úpravy pomocí zvolených algoritmů a průběhů chyby při následném učení.
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