A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others. © 2007 Elsevier Inc. All rights reserved

Efficient Graph Coloring with Parallel Genetic Algorithms

In this paper a new parallel genetic algorithm for coloring graph vertices is presented. In the algorithm we apply a migration model of parallelism and define two new recombination operators SPPX and CEX. For comparison two problem-oriented crossover operators UISX and GPX are selected. The performance of the algorithm is verified by computer experiments on a set of standard graph coloring instances

Heuristics for Multi-Population Cultural Algorithm

Cultural Algorithm (CA) is one of the Evolutionary Algorithms (EAs) which de- rives from the cultural evolution process in nature. As an extended version of the CA, the Multi-population Cultural Algorithm (MPCA) has multiple population spaces. Since the evolutionary information can be exchanged among the sub-populations, the MPCA can obtain better results than the CA in optimization problems. In this thesis, we introduce heuristics to improve the MPCA. The heuristic strate- gies target the existing weaknesses in MPCAs. Four strategies are developed address- ing these weaknesses, including the individual memory heuristic, the social interaction heuristic, the dynamic knowledge migration interval heuristic and the population dis- persion based knowledge migration interval heuristic.Five standard benchmark opti- mization functions with di erent characteristics are taken to test the e ciency of the heuristics. Simulation results show that each heuristic, to varying degrees, improves the MPCA in convergence speed, stability and precision. We compared di erent combinations of the strategies, and the results show that the MPCAs with social interaction based knowledge selection, as well as dynamic knowledge migration inter- val/population dispersion based knowledge migration interval, outperform the other combinations in both low-dimension functions and high-dimension functions

Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto

Wideband Dual-Circular-Polarization Antennas for Millimetre-Wave Wireless Communications.

PhD Theses.Millimetre-wave (mmWave) wireless communications has attracted great interest in recent years as a promising technology that can provide high data rate beyond 5G. Circular Polarization (CP) radiation is preferable to Linear Polarization (LP) in mmWave wireless communications, owing to the reliability of the wireless link it provides to suppress multi-path fading and polarization misalignment. Apart from the link robustness, high link capacity is also desirable by introducing technologies such as Polarization Division Multiplexing (PDM) or In-Band Full-Duplex (IBFD). Therefore, this research aims to design dual-circular-polarization (dual-CP) antennas with wide bandwidth and high port isolation to enable PDM or IBFD for mmWave wireless communications thereby achieving twofold spectral e ciency. The research work has been conducted in the following four parts. Firstly, a dual-CP horn antenna based on a stepped septum polarizer is designed in the W-band. By optimising the horn pro le, a wide bandwidth with good isolation is achieved in simulation and veri ed in experiment. Secondly, to further push the limits of the dual-CP antenna based on the stepped septum polarizer, a grooved-wall septum polarizer is proposed for the rst time with a 2-step design method to realize a dual-CP antenna with wider operating bandwidth and higher port isolation. Thirdly, in order to ease the fabrication di culty and further improve the antenna performance, a novel grooved-wall CP horn antenna is designed in simulation and veri ed in experiment in the W-band. The dual CP performance can be generated when used with an Orthomode Transducer (OMT), instead of a septum. Finally, this septum-free approach has been generalised to design a multi-section groovedi wall CP horn antenna with a low re ection coe cient over a wide bandwidth in the W-band. This horn antenna is demonstrated to be capable of achieving dual-CP with high isolation over a wide bandwidth when used together with an OMT