The use of some non-minimal representations to improve the effectiveness of genetic algorithms

In the unitation representation used in genetic algorithms, the number of genotypes that map onto each phenotype varies greatly. This leads to an attractor in phenotype space which impairs the performance of the genetic algorithm. The attractor is illustrated theoretically and empirically. A new representation, called the length varying representation (LVR), allows unitation chromosomes of varying length (and hence with a variety of attractors) to coexist. Chromosomes whose lengths yield attractors close to optima come to dominate the population. The LVR is shown to be more effective than the unitation representation against a variety of fitness functions. However, the LVR preferentially converges towards the low end of phenotype space. The phenotype shift representation (PSR), which retains the ability of the LVR to select for attractors that are close to optima, whilst using a fixed length chromosome and thus avoiding the asymmetries inherent in the LVR, is defined. The PSR is more effective than the LVR and the results compare favourably with previously published results from eight other algorithms. The internal operation of the PSR is investigated. The PSR is extended to cover multi-dimensional problems. The premise that improvements in performance may be attained by the insertion of introns, non-coding sequences affecting linkage, into traditional bit string chromosomes is investigated. In this investigation, using a population size of 50, there was no evidence of improvement in performance. However, the position of the optima relative to the hamming cliffs is shown to have a major effect on the performance of the genetic algorithm using the binary representation, and the inadequacy of the traditional crossover and mutation operators in this context is demonstrated. Also, the disallowance of duplicate population members was found to improve performance over the standard generational replacement strategy in all trials

Aramid Nanofiber Composites for Energy Storage Applications

Lithium ion batteries and non-aqueous redox flow batteries represent two of the most important energy storage technologies to efficient electric vehicles and power grid, which are essential to decreasing U.S. dependence on fossil fuels and sustainable economic growth. Many of the developmental roadblocks for these batteries are related to the separator, an electrically insulating layer between the cathode and anode. Lithium dendrite growth has limited the performance and threatened the safety of lithium ion batteries by piercing the separator and causing internal shorts. In non-aqueous redox flow batteries, active material crossover through microporous separators and the general lack of a suitable ion conducting membrane has led to low operating efficiencies and rapid capacity fade. Developing new separators for these batteries involve the combination of different and sometimes seemingly contradictory properties, such as high ionic conductivity, mechanical stability, thermal stability, chemical stability, and selective permeability. In this dissertation, I present work on composites made from Kevlar-drived aramid nanofibers (ANF) through rational design and fabrication techniques. For lithium ion batteries, a dendrite suppressing layer-by-layer composite of ANF and polyethylene oxide is present with goals of high ionic conductivity, improved safety and thermal stability. For non-aqueous redox flow batteries, a nanoporous ANF separator with surface polyelectrolyte modification is used to achieve high coulombic efficiencies and cycle life in practical flow cells. Finally, manufacturability of ANF based separators is addressed through a prototype machine for continuous ANF separator production and a novel separator coated on anode assembly. In combination, these studies serve as a foundation for addressing the challenges in separator engineering for lithium ion batteries and redox flow batteries.PHDMacromolecular Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138543/1/situng_1.pd

Co-orthologs of KATANIN1 Impact Plant Morphology and Show Differential Evolution in Maize

Understanding how the size and shape of crop plants and their specific organs are genetically controlled may allow for the development of cultivars with improved plant architecture. A microtubule-severing enzyme called katanin p60 is encoded by KATANIN1 (KTN1) in Arabidopsis or by an ortholog, dwarf and gladius leaf1 (dgl1), in rice. Katanin p60 has been implicated in the control of anisotropic cell growth, which is cell growth directed in a specific direction instead of equally in all directions. Anisotropic cell growth is crucial for proper plant shape and its disruption in ktn1/dgl1 mutants leads to morphological changes such as stunted plant height, shorter leaves and reduced inflorescence size

Simulated Experince Evaluation in Developing Multi-agent Coordination Graphs

Cognitive science has proposed that a way people learn is through self-critiquing by generating \u27what-if\u27 strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are more likely to be included in the next generation. The simulation-informed genetic program is evaluated in two domains: sequence matching and Robocode. Results indicate the algorithm does not perform equally in all environments. In sequence matching, experiential evaluation fails to perform better than the control. However, in Robocode, the experiential evaluation method initially outperforms the control then stagnates and often regresses. This is likely an indication that the algorithm is over-learning a single solution rather than adapting to the environment and that learning through simulation includes a satisficing component

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions

Skills, organisational performance and economic activity in the hospitality industry : a literature review

This monograph aims to understand the pressures which push organisations to adopt particular routes to competitive advantage. The monograph aims to discover if the best practice high skill, high wage and high quality route is used in the hospitality industry. It seeks to determine the influence of companies' product market strategies and their in-company and external structural factors on skills levels, work organisation, job design and people management systems. The monograph looked at the notion of best practice approaches and then moved on to consider the best way to carry forward the future research agenda of reviewing the nature of human resource management (HRM) in the hospitality sector. Conclusions were drawn from a range of interviews and from existing work which has sought to address the issue of HRM in the hospitality sector

Genetic algorithms in timetabling and scheduling

Thio thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems arc very hard in general, and GAs offer a useful and successful alternative to existing techniques.A framework is presented for GAs to solve modular timetabling problems in edu¬ cational institutions. The approach involves three components: declaring problemspecific constraints, constructing a problem specific evaluation function and using a problem-independent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach rclicG for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA with standard operators.A framework for GAs in job shop and open shop scheduling is also presented. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations implicitly encode a schedule by encoding instructions for a schedule builder. The general robustness of this approach is demonstrated with respect to experiments on a range of widely-used benchmark problems involving many different schedule quality criteria. When compared against a variety of common heuristic search approaches, the GA approach is clearly the most successful method overall. An extension to the representation, in which choices of heuristic for the schedule builder arc also incorporated in the chromosome, iG found to lead to new best results on the makespan for some well known benchmark open shop scheduling problems. The general approach is also shown to be readily extendable to rescheduling and dynamic scheduling

Coding Strategies for Genetic Algorithms and Neural Nets

The interaction between coding and learning rules in neural nets (NNs), and between coding and genetic operators in genetic algorithms (GAs) is discussed. The underlying principle advocated is that similar things in "the world" should have similar codes. Similarity metrics are suggested for the coding of images and numerical quantities in neural nets, and for the coding of neural network structures in genetic algorithms. A principal component analysis of natural images yields receptive fields resembling horizontal and vertical edge and bar detectors. The orientation sensitivity of the "bar detector" components is found to match a psychophysical model, suggesting that the brain may make some use of principal components in its visual processing. Experiments are reported on the effects of different input and output codings on the accuracy of neural nets handling numeric data. It is found that simple analogue and interpolation codes are most successful. Experiments on the coding of image data demonstrate the sensitivity of final performance to the internal structure of the net. The interaction between the coding of the target problem and reproduction operators of mutation and recombination in GAs are discussed and illustrated. The possibilities for using GAs to adapt aspects of NNs are considered. The permutation problem, which affects attempts to use GAs both to train net weights and adapt net structures, is illustrated and methods to reduce it suggested. Empirical tests using a simulated net design problem to reduce evaluation times indicate that the permutation problem may not be as severe as has been thought, but suggest the utility of a sorting recombination operator, that matches hidden units according to the number of connections they have in common. A number of experiments using GAs to design network structures are reported, both to specify a net to be trained from random weights, and to prune a pre-trained net. Three different coding methods are tried, and various sorting recombination operators evaluated. The results indicate that appropriate sorting can be beneficial, but the effects are problem-dependent. It is shown that the GA tends to overfit the net to the particular set of test criteria, to the possible detriment of wider generalisation ability. A method of testing the ability of a GA to make progress in the presence of noise, by adding a penalty flag, is described

SUSTAINABLE LIFETIME VALUE CREATION THROUGH INNOVATIVE PRODUCT DESIGN: A PRODUCT ASSURANCE MODEL

In the field of product development, many organizations struggle to create a value proposition that can overcome the headwinds of technology change, regulatory requirements, and intense competition, in an effort to satisfy the long-term goals of sustainability. Today, organizations are realizing that they have lost portfolio value due to poor reliability, early product retirement, and abandoned design platforms. Beyond Lean and Green Manufacturing, shareholder value can be enhanced by taking a broader perspective, and integrating sustainability innovation elements into product designs in order to improve the delivery process and extend the life of product platforms. This research is divided into two parts that lead to closing the loop towards Sustainable Value Creation in product development. The first section presents a framework for achieving Sustainable Lifetime Value through a toolset that bridges the gap between financial success and sustainable product design. Focus is placed on the analysis of the sustainable value proposition between producers, consumers, society, and the environment and the half-life of product platforms. The Half-Life Return Model is presented, designed to provide feedback to producers in the pursuit of improving the return on investment for the primary stakeholders. The second part applies the driving aspects of the framework with the development of an Adaptive Genetic Search Algorithm. The algorithm is designed to improve fault detection and mitigation during the product delivery process. A computer simulation is used to study the effectiveness of primary aspects introduced in the search algorithm, in order to attempt to improve the reliability growth of the system during the development life-cycle. The results of the analysis draw attention to the sensitivity of the driving aspects identified in the product development lifecycle, which affect the long term goals of sustainable product development. With the use of the techniques identified in this research, cost effective test case generation can be improved without a major degradation in the diversity of the search patterns required to insure a high level of fault detection. This in turn can lead to improvements in the driving aspects of the Half-Life Return Model, and ultimately the goal of designing sustainable products and processes