Evolving Heterogeneous And Subcultured Social Networks For Optimization Problem Solving In Cultural Algorithms

Cultural Algorithms are computational models of social evolution based upon principle of Cultural Evolution. A Cultural Algorithm are composed of a Belief Space consisting of a network of active and passive knowledge sources and a Population Space of agents. The agents are connected via a social fabric over which information used in agent problem solving is passed. The knowledge sources in the Belief Space compete with each other in order to influence the decision making of agents in the Population Space. Likewise, the problem solving experiences of agents in the Population Space are sent back to the Belief Space and used to update the knowledge sources there. It is a dual inheritance system in which both the Population and Belief spaces evolve in parallel over generations. A question of interest to those studying the emergence of social systems is the extent to which their organizational structure reflects the structures of the problems that are presented to them. In a recent study [Reynolds, Che, and Ali, 2010] used Cultural Algorithms as a framework in which to empirically address this and related questions. There, a problem generator based upon Langton\u27s model of complexity was used to produce multi-dimensional real-valued problem landscapes of varying complexities. Various homogeneous social networks were then tested against the range of problems to see whether certain homogeneous networks were better at distributing problem solving knowledge from the Belief Space to individuals in the population. The experiments suggested that different network structures worked better in the distribution of knowledge for some optimization problems than others. If this is the case, then in a situation where several different problems are presented to a group, they may wish to utilize more than one network to solve them. In this thesis, we first investigate the advantages of utilizing a heterogeneous network over a suite of different problems. We show that heterogeneous approaches begin to dominate homogeneous ones as the problem complexity increases. A second heterogeneous approach, sub-culutres, will be introduced by dividing the social fabric into smaller networks. The three different social fabrics (homogeneous, heterogeneous and Sub-Cultures) were then compared relative to a variety of benchmark landscapes of varying entropy, from static to chaotic. We show that as the number of independent processes that are involved in the production of a landscape increases, the more advantageous subcultures are in directing the population to a solution. We will support our results with t-test statistics and social fabric metrics performance analysis

A comprehensive survey on cultural algorithms

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Multi-objective cultural algorithms

Evolutionary algorithms, including the Cultural Algorithms and other bio-inspired approaches are frequently used to solve problems that are not tractable for traditional approaches. Previously, research in the field of evolutionary optimization has focused on single-objective problems. On the contrary, most real-world problems involve more than one objective where these objectives may conflict with each other. The newest implementation of the Cultural Algorithms to solve multi-objective optimization is named MOCAT. It is not the first time that the Cultural Algorithms have been used to solve multi-objective problems. Nonetheless, it is the first time that the Cultural Algorithms systematically merge techniques that have been popular in other evolutionary algorithms, such as non-domination sorting and spacing metrics, among other features. The goal of the thesis is to test whether MOCAT can efficiently handle multi-objective optimization. In addition to that, we want to observe how the knowledge sources and agent topologies within a Cultural Algorithm interact with each other during the problem solving process. The MOCA system was evaluated against the ZDT test set proposed by Zitzler (2000). Some basic results that were produced are as follows: 1. The MOCAT system was very effective in the generation of an appropriate configuration for solving problems with different combinations of these features. Even for a given problem, as information was added to the knowledge sources, adjustments in the topologies could be made effectively. 2. As the complexity of the problems increased in terms of the number of problem features, the MOCAT system\u27s relative performance increased. 3. A problem with just a single problem feature, such as ZDT1 and ZDT5, was often effectively solved by just using one metric guide the solution process. However, if there were multiple problems, combining the two metrics together produced a synergy that outperformed each single metric based system. 4. This synergy resulted from the fact that they rewarded spread production in different ways. The spread metric focused on global distribution while the hyper-volume tended to support local optimization. 5. The configuration of the top performing MOCAT system varied markedly from one problem to the next. Our experiments proved the potential of applying the Cultural Algorithms on multi-objective problems and open a gate to observing internal behaviors of various knowledge sources and social fabrics

Linking design to finance : enabling a co-operative developer platform through automated design and valuation

Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 136-141).Significant shifts in technology and finance are altering the practice and position of urban design and development. These shifts - the torrent of micro-spatialized data, the amplification of designer instrumentality through computation, and the financialization of built capital into abstract securities - are forming a new relational infrastructure propelling the production of the built environment. Currently, coupling these shifts together remains the specialty of well-capitalized and sophisticated institutions, but the march of technological progress forecasts the widespread democratization of urban development skills and knowledge. This thesis explores the potential outcomes from mass accessibility to urban data, design computation, and digitized financing. I present two patent propositions outlining design methods that culminate in a project deploying network effects through collectively-financed, mass-distributed developments. The project is situated in three neighborhoods of New York City, on three-dozen sites for one-thousand inhabitants, and the methodology consists of three design computation processes. The first is a method for the automated re-massing of urban typologies using procedural scripting and a geometry constraint engine, in order to achieve open-space and density targets. The second is the automated valuation of a real estate development project using projected cash flows and construction cost estimations. Lastly, an optimization method matches suites of sites, project-massings, and financing arrangements; demonstrating the ability for the inhabitants' spatial needs to be met within financial constraints. Assuming that these technologies will be in widespread use evokes a vision for clusters of households to collectively originate, fund, and construct networks of mutually co-dependent developments. With the ability to operationalize a co-ownership model of distributed live-work spaces, self-organizing groups will have a dramatically expanded capability to influence the design and use of urban fabric - in practice, a Lefebvrian 'right to the city'.by Daniel Fink.S.M

Graduate Course Descriptions, 2005 Fall

Wright State University graduate course descriptions from Fall 2005