An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems

This thesis presents a new class of evolutionary algorithms called mobile cellular evolutionary algorithms (mcEAs). These algorithms are characterized by individuals moving around on a spatial population structure. As a primary objective, this thesis aims to show that by controlling the population density and mobility in mcEAs, it is possible to achieve much better control over the rate of convergence than what is already possible in existing cellular EAs. Using the observations and results from this investigation into selection pressure in mcEAs, a general architecture for developing agent-based evolutionary algorithms called Artificial Ecosystems (AES) is presented. A simple agent-based EA is developed within the scope of AES is presented with two individual-based bottom-up schemes to achieve dynamic population sizing. Experiments with a test suite of optimization problems show that both mcEAs and the agent-based EA produced results comparable to the best solutions found by cellular EAs --Abstract, page iii

Individual-based artificial ecosystems for design and optimization

Individual-based modeling has gained popularity over the last decade, mainly due to the paradigm\u27s proven ability to address a variety of problems seen in many disciplines, including modeling complex systems from bottom-up, providing relationship between component level and system level parameters, and discovering the emergence of system-level behaviors from simple component level interactions. Availability of computational power to run simulation models with thousands to millions of agents is another driving force in the widespread adoption of individual-based modeling. This thesis proposes an individual-based modeling approach for solving engineering design and optimization problems using artificial ecosystems --Abstract, page iii

Sequential Coupling of The Transesterification of Cyclic Carbonates with The Selective N-Methylation of Anilines Catalysed by Faujasites

Anilines (R¢C6H4NH2: R¢ = H, p-MeO, p-Me; p-Cl, and p-NO2) react with a mixture of ethylene carbonate and methanol at 180 ◦C in the presence of alkali metal exchanged faujasites—preferably of the X-type—to give the corresponding N,N-dimethyl derivatives (R¢C6H4NMe2) in isolated yields up to 98%. Evidence proves that methanol is not the methylating agent. The reaction instead takes place through two sequential transformations, both catalyzed by faujasites: first transesterification of ethylene carbonate with MeOH to yield dimethyl carbonate, followed by the selective N-methylation of anilines by dimethyl carbonate. Propylene carbonate, is less reactive than ethylene carbonate, but it can be used under the same conditions. The overall process is highly chemoselective since the competitive reactions between the anilines and the cyclic carbonates is efficiently ruled out. Ethanol and propanol form the corresponding diethyl- and dipropyl- carbonates in the first step, but these compounds are not successful for the domino alkylation of anilines

Spectral and Spatial Analysis of False Alarms in Background Data

A significant amount of background data was collected as part of May 2005 tests at an arid site for airborne minefield detection. An extensive library of the target chips for MSI (four bands) and MWIR sensors for false alarms and mines was created from this data collection, as discussed in another paper in the same proceeding. In this paper we present some results from the analysis of this background data to determine spectral and shape characteristics of different types of false alarms. Particularly, a set of spectral features is identified that can be used for effective false alarm rejection for the benefit of airborne minefield detection programs. A reasonable separation between vegetation and non-vegetation (like rocks) is shown for Normalized Difference Vegetation Index (NDVI) type metrics. Also, a reasonable separation is shown between different types of false alarms at a given time using Color Contrast feature. The spatial distribution of different types of false alarms, as seen in available airborne background data, is also evaluated and discussed. Such spatial analysis is of interest from the perspective of minefield level detection and analysis. The paper is concluded with a discussion on future directions for this effort