A rigorous evaluation of crossover and mutation in genetic programming

The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure

An application of genetic algorithms to chemotherapy treatment.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation

Lightning Prediction for Space Launch Using Machine Learning Based Off of Electric Field Mills and Lightning Detection and Ranging Data

Kennedy Space Center and Cape Canaveral Air Station, FL, where the Air Force conducts space launches, are in an area of frequent lightning strikes, which is main obstacle in meeting launch goals. The 45th Weather Squadron (45th WS) ensures that any weather safety requirements are met during pre-launch and actual space launch. Using only summer months from three years’ worth of lightning detection and ranging (LDAR) and electric field mill (EFM) data from KSC, several feedforward neural networks are constructed. Separate models are built for each EFM and trained by adjusting parameters to forecast lightning 30 minutes out in the surrounding area of each field mill

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Deriving Goal-oriented Performance Models by Systematic Experimentation

Performance modelling can require substantial effort when creating and maintaining performance models for software systems that are based on existing software. Therefore, this thesis addresses the challenge of performance prediction in such scenarios. It proposes a novel goal-oriented method for experimental, measurement-based performance modelling. We validated the approach in a number of case studies including standard industry benchmarks as well as a real development scenario at SAP