Thesis (M.S.)--Mississippi State University. Department of Industrial Engineering. / Title from title screen. Includes bibliographical references.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this dissertation formalizes human creative problem solving as competitive multi-agent search. It differs from existing single-agent and team-search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape caused by these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for competitive multi-agent search. This hypothesis is verified in experiments using an abstract domain based on the NK model, i.e. partially correlated and tunably rugged fitness landscapes, and a concrete domain in the form of a social innovation game. In both domains, different specialized strategies are evolved for each different competitive environment, and also strategies that generalize across environments. Strategies evolved in the abstract domain are more effective and more complex than hand-designed strategies and one based on traditional tree search. Using a novel spherical visualization of the fitness landscapes of the abstract domain, insight is gained about how successful strategies work, e.g. by tracking positive changes in the landscape. In the concrete game domain, human players were modeled using backpropagation, and used as opponents to create environments for evolution. Evolved strategies scored significantly higher than the human models by using a different proportion of actions, providing insights into how performance could be improved in social innovation domains. The work thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future. / text

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The aim of this thesis is to evaluate the use of Search-based Procedural Content generation (SBPCG) to help a designer create levels for different game styles. I show how SBPCG can be used for level generation in different game genres by surveying both paper and released commercial solutions. I then provide empirical data by using a Genetic Algorithm (GA) to evolve levels in two different game types, first one being a space puzzle game, and the second a platform game. Constraints from a level designer provide a base to create fitness functions for both games with success. Even though difficulties with level representation make it hard for a designer to work with this technique directly, the generated levels show that the technique has promising potential to aid level designers with their work.

Multi-objective optimization metaheuristics (MOMs) are powerful methods for solving complex optimization problems but can require a large number of function evaluations to find optimal solutions. Thus, an efficient multi-objective optimization method should generate accurate and diverse solutions in a timely manner. Improving MOMs convergence speed is an important and challenging research problem which is the scope of this thesis. This thesis conducted the most comprehensive comparative study ever in MOMs. Based on the results, multi-objective (MO) versions of particle swarm optimization (PSO) and differential evolution (DE) algorithms achieved the highest performances; therefore, these two MOMs have been selected as bases for further acceleration in this thesis. To accelerate the selected MOMs, this work focuses on the incorporation of leadership concept to MO variants of DE and PSO algorithms. Two complex case studies of MO design of renewable energy systems are proposed to demonstrate the efficiency of the proposed MOMs. This thesis proposes three new MOMs, namely, leader and speed constraint multi-objective PSO (LSMPSO), opposition-based third evolution step of generalized DE (OGDE3), and multi-objective DE with leadership enhancement (MODEL) which are compared with seven state-of-the-art MOMS using various benchmark problems. LSMPSO was found to be the fastest MOM for the problem undertaken. Further, LSMPSO achieved the highest solutions accuracy for optimal design of a photovoltaic farm in Toronto area. OGDE3 is the first successful application of OBL to a MOM with single population (no-coevolution) using leadership and self-adaptive concepts; the convergence speed of OGDE3 outperformed the other MOMs for the problems solved. MODEL embodies leadership concept into mutation operator of GDE3 algorithm. MODEL achieved the highest accuracy for the 30 studied benchmark problems. Furthermore, MODEL achieved the highest solution accuracy for a MO optimization problem of hydrogen infrastructures design across the province Ontario between 2008 and 2025 considering electricity infrastructure constraints.

Evolvable hardware (EHW) uses simulated evolution to generate an electronic circuit with specific characteristics, and is generally implemented on Field Programmable Gate Arrays (FPGAs). EHW has proven to be successful at producing small novel circuits for applications such as robot control and image processing, however, traditional approaches, in which the FPGA configuration is directly encoded on the chromosome, have not scaled well with increases in problem and FPGA architecture complexity. One of the methods proposed to overcome this is the incorporation of a growth process, known as morphogenesis, into the evolutionary process. However, existing approaches have tended to abstract away the underlying architectural details, either to present a simpler virtual FPGA architecture, or a biochemical model that hides the relationship between the cellular state and the underlying hardware. By abstracting away the underlying architectural details, EHW has moved away from one of its key strengths, that being to allow evolution to discover novel solutions free of designer bias. Also, by separating the biological model from the target FPGA architecture, too many assumptions and arbitrary decisions need to be made, which are liable to lead to the growth process failing to produce the desired results. In this thesis a new approach to applying morphogenesis to gate-level FPGA- based EHW is presented, whereby circuit growth is closely tied to the underlying gate-level architecture, with circuit growth being driven largely by the state of gate-level resources of the FPGA. An investigation into the applicability of biological processes, structures and mechanisms to morphogenetic EHW (MGEHW) is conducted, and the resulting design elaborated. The developed MGEHW system is applied to solving a signal routing problem with irregular and severe constraints on routing resources. It is shown that the morphogenetic approach outperforms a traditional EHW approach using a direct encoding, and importantly, is able to scale to larger, more complex, signal routing problems without any significant increase in the number of generations required to find an optimal solution. With the success of the MGEHW system in solving primarily structural prob- lems, it is then applied to solving a combinatorial function problem, specifically a one-bit full adder, with a more complete set of FPGA resources. The results of these experiments, together with the previous experiments, has provided valuable information that when analysed has enabled the identification of the critical factors that determine the likelihood of an EHW problem being solvable. In particular this has highlighted the importance of effective fitness feedback for guiding evolution towards its desired goal. Results indicate that the gate-level morphogenetic approach is promising. The research presented here is far from complete; many avenues for future research have opened. The MGEHW system that has been developed allows further research in this area to be explored experimentally. Some of the most fruitful directions for future research are described.

Traditionally, an evolutionary algorithm (EA) operates on a single population with no restrictions on possible mating pairs. Interesting changes to the behaviour of EAs emerge when the structure of the population is altered so that mating between individuals is restricted. Variants of EAs that use such populations are grouped into the field of spatially-structured EAs (SSEAs). Previous research into the behaviour of SSEAs has primarily focused on the impact space has on the selection pressure in the system. Selection pressure is usually characterised by takeover times and the ratio between the neighbourhood size and the overall dimension of space. While this research has given indications into where and when the use of an SSEA might be suitable, it does not provide a complete coverage of system behaviour in SSEAs. This thesis presents new research into areas of SSEA behaviour that have been left either unexplored or briefly touched upon in current EA literature. The behaviour of genetic drift in finite panmictic populations is well understood. This thesis attempts to characterise the behaviour of genetic drift in spatially-structured populations. First, an empirical investigation into genetic drift in two commonly encountered topologies, rings and torii, is performed. An observation is made that genetic drift in these two configurations of space is independent of the genetic structure of individuals and additive of the equivalent-sized panmictic population. In addition, localised areas of homogeneity present themselves within the structure purely as a result of drifting. A model based on the theory of random walks to absorbing boundaries is presented which accurately characterises the time to fixation through random genetic drift in ring topologies. A large volume of research has gone into developing niching methods for solving multimodal problems. Previously, these techniques have used panmictic populations. This thesis introduces the concept of localised niching, where the typically global niching methods are applied to the overlapping demes of a spatially structured population. Two implementations, local sharing and local clearing are presented and are shown to be frequently faster and more robust to parameter settings, and applicable to more problems than their panmictic counterparts. Current SSEAs typically use a single fitness function across the entire population. In the context of multimodal problems, this means each location in space attempts to discover all the optima. A preferable situation would be to use the inherent spatial properties of an SSEA to localise optimisation of peaks. This thesis adapts concepts from multiobjective optimisation with environmental gradients and applies them to multimodal problems. In addition to adapting to the fitness landscape, individuals evolve towards their preferred environmental conditions. This has the effect of separating individuals into regions that concentrate on different optima with the global fitness function. The thesis also gives insights into the expected number of individuals occupying each optima in the problem. The SSEAs and related models developed in this thesis are of interest to both researchers and end-users of evolutionary computation. From the end-user�s perspective, the developed SSEAs require less a priori knowledge of a given problem domain in order to operate effectively, so they can be more readily applied to difficult, poorly-defined problems. Also, the theoretical findings of this thesis provides a more complete understanding of evolution within spatially-structured populations, which is of interest not only to evolutionary computation practitioners, but also to researchers in the fields of population genetics and ecology.

This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramatically reduces the training time. In its core, QiSNN employs the Thorpe neural model that allows the efficient simulation of even large networks. In QiSNN, the presence or absence of features is represented by a string of concatenated bits, while the parameters of the neural network are continuous. For the exploration of these two entirely different search spaces, a novel Estimation of Distribution Algorithm (EDA) is developed. The method maintains a population of probabilistic models specialised for the optimisation of either binary, continuous or heterogeneous search spaces while utilising a small and intuitive set of parameters. The EDA extends the Quantum-inspired Evolutionary Algorithm (QEA) proposed by Han and Kim (2002) and was named the Heterogeneous Hierarchical Model EDA (hHM-EDA). The algorithm is compared to numerous contemporary optimisation methods and studied in terms of convergence speed, solution quality and robustness in noisy search spaces. The thesis investigates the functioning and the characteristics of QiSNN using both synthetic feature selection benchmarks and a real-world case study on ecological modelling. By evolving suitable feature subsets, QiSNN significantly enhances the classification accuracy of eSNN. Compared to numerous other feature selection techniques, like the wrapper-based Multilayer Perceptron (MLP) and the Naive Bayesian Classifier (NBC), QiSNN demonstrates a competitive classification and feature selection performance while requiring comparatively low computational costs.

This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramatically reduces the training time. In its core, QiSNN employs the Thorpe neural model that allows the efficient simulation of even large networks. In QiSNN, the presence or absence of features is represented by a string of concatenated bits, while the parameters of the neural network are continuous. For the exploration of these two entirely different search spaces, a novel Estimation of Distribution Algorithm (EDA) is developed. The method maintains a population of probabilistic models specialised for the optimisation of either binary, continuous or heterogeneous search spaces while utilising a small and intuitive set of parameters. The EDA extends the Quantum-inspired Evolutionary Algorithm (QEA) proposed by Han and Kim (2002) and was named the Heterogeneous Hierarchical Model EDA (hHM-EDA). The algorithm is compared to numerous contemporary optimisation methods and studied in terms of convergence speed, solution quality and robustness in noisy search spaces. The thesis investigates the functioning and the characteristics of QiSNN using both synthetic feature selection benchmarks and a real-world case study on ecological modelling. By evolving suitable feature subsets, QiSNN significantly enhances the classification accuracy of eSNN. Compared to numerous other feature selection techniques, like the wrapper-based Multilayer Perceptron (MLP) and the Naive Bayesian Classifier (NBC), QiSNN demonstrates a competitive classification and feature selection performance while requiring comparatively low computational costs.