Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling

A university course timetabling problem is a combination of optimization problems. The problems are more challenging when a set of events need to be scheduled in the time slot, to be located to the suitable rooms, which is subjected to several sets of hard and soft constraints. All these constraints that exist as regulations within each resource for the event need to be fulfilled in order to achieve the optimum tasks. In addition, the design of course timetables for universities is a very difficult task because it is a non-deterministic polynomial, (NP) hard problem. This problem can be minimized by using a Micro Genetic Algorithm approach. This approach, encodes a chromosome representation as one of the key elements to ensure the infeasible individual chromosome produced is minimized. Thus, this study proposes an encoding chromosome representation using one-dimensional arrays to improve the Micro Genetic algorithm approach to soft constraint problems in the university course schedule. The research contribution of this study is in developing effective and feasible timetabling software using Micro Genetic Algorithm approach in order to minimize the production of an infeasible individual chromosome compared to the existing optimization algorithm for university course timetabling where UNITAR International University have been used as a data sample. The Micro Genetic Algorithm proposed has been tested in a test comparison with the Standard Genetic algorithm and the Guided Search Genetic algorithm as a benchmark. The results showed that the proposed algorithm is able to generate a minimum number of an infeasible individual chromosome. The result from the experiment also demonstrated that the Micro Genetic Algorithm is capable to produce the best course schedule to the UNITAR International University

Комбінований компетентний паралельний генетичний алгоритм та його застосування для задачі побудови розкладів

Розглянуто компетентні генетичні алгоритми, що навчаються зв’язності, а також способи паралельної реалізації генетичних алгоритмів. Запропоновано власний алгоритм, що об’єднує методики побудови генетичних алгоритмів, та репрезентовано спосіб розв’язання NP-повної задачі складання розкладу занять в університеті

Genetic algorithm for parameter and scale selection to predict soil moisture patterns

Soil moisture is a critical component of hydrological processes, and its spatio-temporal distribution depends on many geographical factors (such as elevation, slope, and aspect, etc.). Each of the factors is likely influential over a different scale and to a different degree. Near-surface soil moisture data were collected across a working 10-ha field southwest of Ames, IA in growing seasons of 2004 to 2007. A genetic algorithm is developed to compare geographical factors to the moisture patterns over a range of scales. The genetic algorithm will develop a model in which each factor is computed over a different scale for use in prediction of reference variable. Optimized scales for each parameter are arrived at through successive generations, including crossover and mutation of this evolutionary algorithm. Using this approach, not only are the primary influential relationships uncovered, but the most appropriate scale for comparison to moisture pattern is identified. The results of this analysis can be used to predict the spatio-temporal patterns of soil moisture across a region a priori

Learning Process Enhancement for Robot Behavior

Designing a simulated system and training it to optimize its tasks in simulated environment helps the designers to avoid problems that may appear when designing the system directly in real worl

КОМБІНОВАНИЙ КОМПЕТЕНТНИЙ ПАРАЛЕЛЬНИЙ ГЕНЕТИЧ-НИЙ АЛГОРИТМ ТА ЙОГО ЗАСТОСУВАННЯ ДЛЯ ЗАДАЧІ ПОБУ-ДОВИ РОЗКЛАДІВ

Розглянуто компетентні генетичні алгоритми, що навчаються зв’язності, а також способи паралельної реалізації генетичних алгоритмів. Запропоновано власний алгоритм, що об’єднує методики побудови генетичних алгоритмів, та репрезентовано спосіб розв’язання NP-повної задачі складання розкладу занять в університеті

Application of Micro-Genetic Algorithm for Task Based Computing

Abstract — Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used. I

Decoder based on Parallel Genetic Algorithm and Multi-objective Optimization for Low Density Parity Check Codes

Genetic algorithms are powerful search techniques that are used successfully to solve problems in many different disciplines. This article introduces a new Parallel Genetic Algorithm for decoding LDPC codes (PGAD). The results show that the proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency. We also show that the fitness function must be improved by Multi-objective Optimization, for this, we applied the Weighted Sum method to improve PGAD, this new version is called (MOGAD) gives higher performance compared to one. Keywords: Parallel Genetic Algorithms decoder, Sum-Product decoder, Fitness Function, LDPC codes, Error correcting codes, Multi-objective optimization, Weighted sum method

Genetic algorithms for Hyperspectral Range and Operator Selection

A novel genetic algorithm was developed using mathematical operations on spectral ranges to explore spectral operator space and to discover useful mathematical range operations for relating spectral data to reference parameters. For each range, the starting wavelength and length of the range, and a mathematical range operation were selected with a genetic algorithm. Partial least squares (PLS) regression was used to develop models predicting reference variables from the range operations. Reflectance spectra from corn plant canopies were investigated, with proportion of plants (1) with visible tassels and (2) starting to shed pollen as reference data. PLS models developed using the spectral range operator framework had similar fitness than PLS models developed using the full spectrum. This range/operator framework enabled identification of those spectral ranges with most predictive capability and which mathematical operators were most effective in using that predictive capability. Detection of operator locality may have utility in sensor and algorithm design and in developing breeding stock for other algorithms