For a long time, engineering design research has been focused on the development of various design theories, methodologies, methods, tools, and procedures. The design methods have been subsequently used by engineers to more efficiently design artifacts. However, as the artifacts have grown in complexity, the need for new methods has become obvious. Also, in a nowadays world, increased competition and globalization require organizations to reexamine traditional product development strategies. Traditional methods focused exclusively on the numerical optimality of produced artifacts, or their manufacturing processes, are no longer adequate. Creativity and innovation of designed artifacts provide organizations not only with a competitive advantage but are, in fact, a matter of their survival.

This dissertation addresses this problem by posing and answering the question: "How can one construct an effective method for designing engineering systems that would support development of novel/creative designs and their efficient optimization?" It proposes a new and conceptually coherent design method, called Emergent Engineering Design. The proposed design method is inspired by the fundamental processes occurring in nature, which has arguably created the most fascinating designs known to humankind. All major phases of Emergent Engineering Design are represented by complex systems, including cellular automata and evolutionary algorithms, which have been successfully used to model the processes governing the complex behavior occurring in nature.

In order to facilitate the development of the proposed design method, Emergent Engineering Design was implemented in a computer system called Emergent Designer. It is an integrated research and design support tool which applies models of complex systems to represent engineering systems and analyze design processes. Emergent Designer was used to conduct the empirical validation of the proposed design method for two classes of conceptual design problems in structural engineering. The extensive design experiments reported in this dissertation have shown that Emergent Engineering Design not only generates novel design concepts exhibiting remarkable structural shaping patterns but it also efficiently optimizes them.

This paper discusses the results of extensive design experiments in which memetic algorithms were applied to optimize topologies of steel structural systems in tall buildings. In these experiments, evolutionary algorithms were employed to determine optimal configurations of structural members (topology optimization) while the optimal cross-sections of members (sizing optimization) were found using continuous/discrete optimization algorithm implemented in SODA. The impact of all major evolutionary computation parameters on the performance of memetic algorithms was investigated. Two classes of complex structural design problems were considered: design of a wind bracing system in a tall building and design of the entire steel structural system in a tall building. The total weight of the structural system was assumed as the optimality criterion with respect to which the designs were optimized while satisfying all design requirements specified by appropriate design codes.

In the conducted experiments various key EC parameters and their values were considered having the largest impact on the performance of memetic algorithms. It was discovered that the type of EA, the rate of mutation operator, and the size of parent population were critical for the success of structural optimization processes. Specifically, evolution strategies produced on average significantly better results than genetic algorithms for the design problems considered in the paper. Also, low mutation rates, i.e. 0.025, resulted in best performance of memetic algorithms. Furthermore, small parent population sizes were generally preferred to large populations. For the simpler problem of conceptual design of a wind bracing system, optimal results were produced even when the population with a single member was used. In the case of the second and more complex design problem slightly larger population sizes were required consisting of 5 members.

Results of a large number of design experiments allowed formulating initial recommendations regarding optimal parameter settings for memetic algorithms for structural design applications. The experiments also produced a body of structural design knowledge, both quantitative and qualitative in nature. They identified regions of the design spaces in which high-performance solutions can be found. They also defined the ranges of the total weight of structural systems associated with high-performance solutions for both classes of design problems. Furthermore, significant qualitative differences between high-performance solutions have been identified. The structural shaping patterns exhibited by high-performing designs ranged from crossed macrodiagonal patterns composed of X bracings to irregular patterns consisting of various types of bracings.

This paper explores the state of the art in structural design inspired by nature and proposes an improved understanding of this emerging paradigm and its major components. First, it introduces and discusses the nature of three categories of inspiration, including visual, conceptual, and computational inspiration. Next, several important inspiration sources are identified and briefly described, including Evolutionary Computation, Coevolutionary Computation, Cellular Automata, and TRIZ. In particular, design generation mechanisms based on cellular automata are introduced with some details. They are inspired by the processes of morphogenesis occurring in nature and have great potential to generate novel designs. In this case, the design generation mechanisms are encoded in so-called generative representations, which are also described. Three major design objectives are introduced and discussed, namely optimality, creativity, and robustness. They are related to the sources of inspiration and the corresponding computational mechanisms inspired by nature. Also, three levels of integration of computational mechanisms inspired by nature are proposed and their relationship to design objectives is discussed. Finally, a general design situation, when inspiration by nature is considered, is introduced and its unified description is proposed as the first step in the direction of building a unified approach to structural design inspired by nature. The paper provides initial conclusions and discusses the most promising directions for future research.

This paper provides the initial results of a study on the applications of cellular automata representations in evolutionary design of topologies of steel structural systems in tall buildings. In the paper, a brief overview of the state of the art in cellular automata and evolutionary design representations is presented. Next, morphogenic evolutionary design is introduced and illustrated by several types of cellular automata representations. Further, Emergent Designer, a unique evolutionary design tool developed at George Mason University, is briefly described. It is an integrated research and design support tool which applies models of complex adaptive systems to represent engineering systems and analyze design processes. The paper also reports the initial results of several structural design experiments conducted with Emergent Designer. The objective of the experiments was to determine feasibility of various types of cellular automata representations in topological structural optimization. Finally, initial research conclusions and recommendations for the further research are provided.

The terrorist attacks of September 11, 2001 have sparked renewed interest in developing effective policies and strategies for evacuating densely populated areas. The current analytical tools for dealing with such evacuations are sorely lacking, in both theory and practice. The conceptual model presented in this paper marries the technical areas of cellular automata, evolutionary computation, and transportation science, along with some recent research on infrastructure security, to make significant progress in traffic management and hazard response systems. The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. This new knowledge can be utilized to develop more effective operational strategies and consequently more robust hazard response systems.

Furthermore, the specific research objective is to investigate the formulation and application of cellular automata models of metropolitan transportation systems, with a focus on systems operating under emergency evacuation conditions. The basic context is evacuation of a defined urban area, such as the urban core of Washington, DC under terrorist attacks. The conceptual model proposes the use of evolutionary algorithms to search the space of the evacuation control strategies and determine the most successful strategies for a given urban area.

This paper introduces an integrated research and design support tool, called Emergent Designer, developed at George Mason University. It is a tool that implements models of various complex systems, including cellular automata and evolutionary algorithms, to represent engineering systems and their related design processes. The system is intended for conducting design experiments in the area of structural design and for the analysis of their results. It implements state-of-the-art representations supporting generation of novel design concepts and efficient mechanisms for their subsequent optimization at the topological and sizing levels. The first part of this paper describes the overall system's architecture and the flow of information among its components. The actual system's implementation is discussed next and illustrated with several screen shots of the system's graphical user interface. Emergent Designer's novel approach to representing steel structural systems in tall buildings is also presented. It is based on the use of generative representations which utilize cellular automata to generate design concepts. Several design experiments are briefly described to demonstrate the feasibility of Emergent Designer in conceptual design as well as of design processes modeled by complex systems.

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems' representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.

This paper presents results of a study on evolutionary computation in the design of the steel structural systems of tall buildings. It describes results of extensive research on both short-term (up to a few hundred generations) and long-term evolutionary design processes (at least a few thousand generations). The experiments were conducted with Inventor 2001, an evolutionary design support tool developed at George Mason University, for generating conceptual and detailed designs of steel structural systems in tall buildings. First, the paper discusses conceptual design of steel structural systems in tall buildings and briefly introduces Inventor 2001 as well as its design representation and evolutionary computation characteristics. Next, it provides the results obtained from systematic parametric design experiments conducted with Inventor 2001. The objective of these experiments was to qualitatively and quantitatively investigate evolution of steel structural systems of tall buildings during a multistage evolutionary design process as well as the influence of various evolutionary computation parameters. Mutation and crossover rates, population size, the length of the evolutionary processes, and the importance of symmetry requirement have been analyzed and results produced. Emergence of structural shaping patterns has been also studied and several interesting patterns found in the evolutionary design process. Finally, research conclusions are presented as well as recommendations for further research and development of evolutionary design support tools.

A novel methodology for building tutoring system is proposed. It includes the integration of state of the art computer science methods and tools and the use of an ontology for the core knowledge representation. First, the paper presents the ongoing Information Technology revolution in engineering and the related paradigm changes in education. Next, an overview of the concept of an ontology and its various definitions are provided, along with available ontology development tools. In the following section, an architecture of an ontology-based tutoring system is proposed. As a proof of concept, the proposed architecture has been used in GMU Educator, an intelligent tutoring system developed at George Mason University in the School of Information Technology and Engineering. A detailed description of the GMU Educator is then presented with various examples. Finally, conclusions and plans for further research are provided in the last section of the paper.

This paper presents results of extensive computational experiments in which evolutionary multiobjective algorithms were used to find Pareto-optimal solutions to a complex structural design problem. In particular, Strength-Pareto Evolutionary Algorithm 2 (SPEA2) was combined with a mathematical programming method to find optimal designs of steel structural systems in tall buildings with respect to two objectives (both minimized): the total weight and the maximum horizontal displacement of a tall building. SPEA2 was employed to determine Pareto-optimal topologies of structural members (topology optimization) whose cross-sections were subsequently optimized by the mathematical programming method (sizing optimization). The main contribution of the paper is the determination of the nature of the Pareto front in this two-dimensional objective space and its dependence on the building's aspect ratio. The results reported provide important qualitative and quantitative knowledge regarding the relationship between the two objectives. They also show the trade-offs involved in the process of conceptual and detailed design of complex structural systems in tall buildings.

The terrorist attacks of September 11, 2001 and afterwards have caused renewed interest in developing effective policies and strategies for evacuating densely populated areas. The large-scale evacuations caused by Hurricane Katrina and other recent hurricane events have reinforced this need. Unfortunately, the current analytical tools for dealing with such evacuations are sorely lacking, in both theory and practice. The model and its computational implementation presented in this paper attempt to close this gap and make significant progress in traffic management and hazard response systems.

The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. Initial ideas on building conceptual models of evacuation scenarios utilizing cellular automata, evolutionary computation, and advanced traffic simulators were presented in the authors' previous paper. This paper describes computational implementations of proposed conceptual models. It also discusses preliminary results of several computational experiments in which the models were used to determine robust configurations of traffic control systems operating under emergency conditions. In these experiments, optimal evacuation strategies were sought for vehicles located within a representative urban area affected by various types of terrorist attacks.

Any computational approach to design, including the use of evolutionary algorithms, requires the transformation of the domain-specific knowledge into a formal design representation. This is a difficult and still not completely understood process. Its critical part is the choice of a type of design representation. The paper addresses this important issue by presenting and discussing results of a large number of design experiments in which parameterized and generative representations were used. Particularly, their computational and design related advantages and disadvantages were investigated and compared.

Evolutionary design experiments reported in this paper considered two classes of structural design problems, including the design of a wind bracing system and the design of an entire structural system in a tall building. Parameterized and generative representations of the structural systems were introduced and their basic features discussed. The generative representations investigated in the paper were inspired by the processes of morphogenesis occurring in nature. Specifically, one-dimensional cellular automata were used to develop, or 'grow,' structural designs from the corresponding 'design embryos.'

The conducted research led to three major conclusions. First, generative representations based on cellular automata proved to scale well with the size of the considered design problems. Second, generative representations outperformed parameterized representations in minimizing weight of the structural systems in our problem domain by generating better designs and finding them faster. Finally, extensive experimental studies showed significant differences in optimal settings for evolutionary design experiments for the two representation types. The rate of mutation operator, the size of the parent population, and the type of the evolutionary algorithm were identified as the evolutionary parameters having the largest impact on the performance of evolutionary design processes in our problem domain.

This paper proposes a new approach to representing structural system inspired by various models of complex systems. Several types of generative representations of steel structural systems are provided and empirically investigated. These representations utilize various kinds of cellular automata to generate design concepts of steel structures in tall buildings. In the paper, a brief overview of the state-of-the-art in cellular automata and generative design is presented. Next, several types of generative representations of steel structural systems in tall buildings are described. The paper also reports the results of several design experiments. They have shown that generative representations produce novel structural shaping patterns which are qualitatively different than the patterns obtained using traditionally used parameterized representations. They also significantly improve the performance of evolutionary algorithms optimizing the structural systems. Finally, research conclusions are presented and most promising paths of future research are discussed.

This paper presents initial results of a study on the application of evolutionary multi-objective optimization methods in the design of the steel structural systems of tall buildings. In the paper, a brief overview of the state-of-the-art in evolutionary multi-objective optimization in structural engineering is provided. Next, conceptual design of steel structural systems in tall buildings is overviewed and the representations of steel structural systems used in the paper are discussed. Furthermore, Emergent Designer, a unique evolutionary design tool developed at George Mason University, is briefly described. It is an integrated research and design support tool which applies models of complex adaptive systems to represent engineering systems and to analyze design processes and their results. The paper also presents the results of several multi-objective structural design experiments conducted with Emergent Designer in which steel structural systems in tall buildings were optimized with respect to their total weight and maximum deflection (two-objective minimization problem). The goal of these experiments was to determine feasibility of evolutionary multi-objective optimization of steel structural systems of tall buildings as well as to qualitatively and quantitatively compare the results with the previous findings obtained with single-objective evolutionary optimization methods. Finally, initial research conclusions are presented as well as promising research directions.

The paper introduces an integrated research and design support tool, called Emergent Designer, developed at George Mason University. It is a tool that implements models of various complex systems, including cellular automata and evolutionary algorithms, to represent engineering systems and design processes. The system is intended for conducting design experiments in the area of structural design and for the analysis of their results. It implements state-of-the-art representations supporting generation of novel design concepts and efficient mechanisms for their subsequent optimization at the topology and sizing level. It also implements advanced methods, models, and tools from statistics and from the linear as well as nonlinear time series analysis to conduct the analysis of the design processes. Thus, it is a versatile tool that can be used both as a state-of-the-art design support tool and as an advanced research tool equipped with the methods and tools for the analysis of the design processes and of the obtained experimental results.

This paper provides the initial results of a study on the applications of generative cellular automata-based representations in evolutionary structural design. First, recent developments in evolutionary design representations and an overview of cellular automata are presented. Next, a complex problem of topological design of steel structural systems in tall buildings is briefly described. Further, morphogenic evolutionary design is introduced and exemplified by cellular automata representations. The paper also reports the initial results of several structural design experiments whose objective was to determine feasibility of the proposed approach. Finally, initial research conclusions are provided.

The goal of this project was to introduce NKS to engineering design problems and estimate a true potential of this approach. It was an initial step in exploring the world of simple programs for engineering design applications as well as introducing a novel methodology presented in Wolfram's A New Kind of Science. The motivation for this research was based on the fact that even designers of complex and sophisticated engineering systems (bridges, tall buildings, space structures, etc.) use only a relatively small set of design/decision rules to develop design concepts. Hence, it is plausible that even very complex designs of engineering systems can be modeled using simple programs like cellular automata (CA).

Two potential ways of attacking this problem are based on the following observations. First, one of the important problems in engineering design is the problem of topological optimum design, where one seeks the optimal configuration of design elements satisfying some constraints, and optimizing a certain objective function, e.g. deflection, or weight, of a steel structure. This search for the optimal configuration of design elements sometimes yields very interesting patterns. It is possible that the search for such interesting structural patterns can be vastly enhanced using CA and other simple programs. Second, it is usually the case that engineering designs have repetitive forms. CA can generate both very simple and repetitive behavior as well as complex forms and configurations. It is definitely worth exploring whether the complex configurations of engineering systems will be better than traditional designs.

The initial exploration of the space of simple programs has been focused on one-dimensional CA (1D CA) and two-dimensional CA (2D CA). 1D CA have been used to generate design concepts of wind bracing systems in tall buildings. These experiments were performed using both elementary CA where two possible types of bracings were used and more complex 1D CA involving 7 possible types of bracing elements. In another set of experiments 2D CA were used. Several types of totalistic 2D CA were studied, including Moore neighborhood and von Neumann neighborhood. The fitness of the generated design concepts was determined based on rigidity of the structural systems and measured by their maximum deflection.

Results of the conducted experiments have shown that CA can generate interesting structural shaping patterns. They included both traditionally known patterns for this class of buildings like vertical and horizontal trusses, but also some novel arrangements of wind bracings characterized by high fitness values. The best results in the reported experiments were obtained using 1D CA. 2D CA also have the potential of producing interesting structural patterns but the search space is vastly larger compared to the 1D CA case and much larger number of experiments is necessary.

This paper presents results of a study on distributed, or parallel, evolutionary computation in the topological design of steel structural systems in tall buildings. It describes results of extensive experimental research on various parallel evolutionary architectures applied to a complex structural design problem. The experiments were conducted using Inventor 2003, a network-based evolutionary design support tool developed at George Mason University. First, a general introduction to evolutionary computation is provided with an emphasis on recent developments in parallel evolutionary architectures. Next, a discussion of conceptual design of steel structural systems in tall buildings is presented. Further, Inventor 2003 is briefly introduced as well as its design representation and evolutionary computation characteristics. Next, the results obtained from systematic design experiments conducted with Inventor 2003 are discussed. The objective of these experiments was to qualitatively and quantitatively investigate evolution of steel structural systems in tall buildings during a distributed evolutionary design process as well as to compare efficiency and effectiveness of various parallel evolutionary architectures with the traditional evolutionary design approaches. Two connectivity topologies (ring topology and fully-connected topology) have been investigated for four populations of structural designs evolving in parallel and using various migration strategies. Also, results of the initial sensitivity studies are reported in which two ways of initializing distributed evolutionary design processes were investigated, using either arbitrarily selected designs as initial parents or randomly generated ones. Finally, initial research conclusions are presented.

The objectives of this working paper are to propose a general concept of proactive security in the context of co-evolutionary computation and to briefly discuss the initial results of research recently began. First, the paper provides an overview of infrastructure security in the context of asymmetric threats. Next, concepts of proactive security are proposed based on co-evolution of terrorist scenarios and security plans. The paper also presents an outline of generation of terrorist scenarios in the context of conceptual design. Finally, it describes TerrorMax/Capitol Hill, a demonstration system being developed for dealing with the generation of terrorist scenarios related to the Capitol Hill in Washington DC. The paper also provides initial discussions of this recently initiated project.

This paper describes a new design paradigm, evolutionary structural design, that involves the entire design process, including conceptual and detailed design stages. In this paper, first a brief overview of the fundamentals of evolutionary computation is provided. Next, the concept of evolutionary structural design and its principia are discussed. Inventor 2001 is described in the following section. It is an experimental research and design system based on evolutionary computation. The system has been developed by the authors at George Mason University for applications in the design of tall buildings. Inventor 2001 allows for the conducting of evolutionary structural design, including the generation of structural concepts and the detailed design, analysis of internal forces, dimensioning, and optimization. Selected specific research results are also provided, including a discussion of the discovered emergent structural shaping patterns that are surprisingly consistent with the state of the art in structural shaping of steel skeleton structures of tall buildings. Finally, the initial research conclusions are provided.

Engineering education is undergoing significant changes, mostly driven by the ongoing Information Technology Revolution. One of the most promising technologies becoming available to educators is that of intelligent software agents. Such agents can be used in building tutoring systems, which in this way become intelligent in terms of knowledge content, their ability to learn (acquire knowledge), and to adapt to the needs and learning preferences of their users.

The paper discusses the development of a prototype intelligent tutoring system, called "George Mason University Intelligent Educator." It has been built as a result of a complex process in which various software development tools have been used in a novel way, including ConceptMap, Protégé2000, Macromedia Flash and JRun. The reported research is part of a NASA-sponsored project in the area of engineering education called "Hierarchical Learning Network" and is coordinated by the Old Dominion University in Virginia. The primary objective of the research at George Mason University is to develop a methodology for building intelligent tutoring systems and to demonstrate its feasibility.

First, the paper provides a brief description of the process that was used for knowledge acquisition and its representation in the form of an ontology. The description includes examples from the area of personal air vehicles. Next, the development process for the proposed intelligent tutoring system is presented, along with a discussion of the integration of various software development tools. Finally, the initial research conclusions and plans for further research are discussed.

The paper discusses a study on the application of intelligent agents (IAs) to conceptual designing. It provides an overview of the state-of-the-art in the areas of ontologies and IAs. Next, the system Disciple, a learning intelligent agent shell, and the system Inventor 2001, evolutionary design support tool, both developed at George Mason University, are briefly presented. Further, the paper introduces the developed ontology for a class of steel skeleton structures of tall buildings. This ontology was used to build an IA for the selection of initial parent design concepts in evolutionary designing. A description of the developed agent is provided as well. Finally, examples of design concepts proposed by the agent are presented. The paper also contains conclusions and recommendations for further research.

This paper discusses the results of extensive computational experiments involving evolutionary developmental representations for structural design. In particular, it describes developmental representations based on one-dimensional cellular automata applied to three complex structural design problems in the domain of steel structural systems in tall buildings. In the experiments conducted with the developmental encodings, several of their key parameters were tested, including the impact of the configuration of the design embryo and the importance of the symmetry constraint on the quality of produced designs. The obtained results were also compared to the results of evolutionary design experiments involving traditionally used direct representations. These comparisons focused primarily on the compactness and evolvability properties of both types of design representations. The results have shown that developmental encodings produce quantitatively better results for the majority of the investigated design problems and also generate structural designs with distinct shaping patterns.

The terrorist attacks of September 11, 2001 have sparked renewed interest in developing effective policies and strategies for evacuating densely populated areas. The current analytical tools for dealing with such evacuations are sorely lacking, in both theory and practice. The conceptual model presented in this paper marries the technical areas of cellular automata, evolutionary computation, and transportation science, along with some recent research on infrastructure security, to make significant progress in traffic management and hazard response systems. The overall goal of this research is to develop a fundamental understanding of the evolutionary and emergent behavior of transportation systems that are operating under emergency evacuation conditions. This new knowledge can be utilized to develop more effective operational strategies and consequently more robust hazard response systems.

Furthermore, the specific research objective is to investigate the formulation and application of cellular automata models of metropolitan transportation systems, with a focus on systems operating under emergency evacuation conditions. The basic context is evacuation of a defined urban area, such as the urban core of Washington, DC under terrorist attacks. The conceptual model proposes the use of evolutionary algorithms to search the space of the evacuation control strategies and determine the most successful strategies for a given urban area.

The main objective of the paper is to propose several novel approaches to security of complex infrastructure systems, which can be utilized in the development of a class of computer tools for infrastructure protection. First, the paper introduces the concept of proactive infrastructure security and compares it with reactive security. The comparison is done in the context of the generation and evaluation of both the terrorist and security scenarios, which are also introduced and described. Next, the paper discusses both the evolutionary and co-evolutionary generation of terrorist and security scenarios and discusses various computer tools, which have been developed at George Mason University for infrastructure protection. Finally, the paper briefly overviews the concept of cellular automata and proposes how cellular automata could be used in the development of computer tools for infrastructure protection. The paper ends with the initial research conclusions and various suggestions for further research.