Optimization of seasonal irrigation scheduling by genetic algorithms

In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal solutions. Second, we analyze the relationship between GA control parameters (population size, crossover rate, and mutation rate) and performance. We identify a combination of population, mutation, and crossover which searched the fitness landscape efficiently. The results suggest that smaller populations are able to provide better performance at relatively low mutation rates. More stable outcomes were generated using low mutation rates. Without crossover the quality of solutions were generally impaired, and the search process was lengthened. Aside from crossover rate zero, no other crossover rates significantly differed. The behaviors observed for best, online, offline, and average performances were sensitive to the combined influences control parameters. Interaction among control parameters was strongly indicated. Finally, several adaptive penalty techniques are presented for handling constraints in GAs, and their effectiveness is demonstrated. The constant penalty function suffered from sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints. The adaptive penalty functions utilizes violation distance based metrics and search time based scaling using generation or trials number, and fitness values to penalize infeasible solutions, as the distance from the feasible region or number of generations increases so does the penalty. They were quite successful in providing solutions with minimal effort. They adapt the penalty as the search continues, encouraging feasible solutions to emerge over the time. Adaptive approaches presented here are flexible, efficient, and robust to parameter settings

An Investigation of Supervised Learning in Genetic Programming

Centre for Intelligent Systems and their Applicationsstudentship 9314680This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples.In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation.The main contributions of this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. to establish the capability of a GP individual using SL, it must be tested or evaluated against each example in the set of training examples

The statistical dynamics of epochal evolution

In this thesis, a new mathematical formalism for analyzing evolutionary dynamics is developed. This formalism combines ideas and methods from statistical mechanics, mathematical population genetics, and dynamical systems theory to describe the dynamics of evolving populations. In particular, the work shows how the maximum entropy formalism of statistical mechanics can be extended to apply to simple evolutionary systems, such that "macroscopic" equations of motion can be constructed from an underlying "microscopic" evolutionary dynamics. More specifically, the thesis studies "epochal evolution"; a dynamical phenomenon which is frequently observed in evolutionary dynamics. In epochal evolution, some macroscopic state variables that describe the evolving population exhibit an alternation of periods of stasis (epochs) and sudden transitions (innovations). For populations evolving in a constant selective environment there are two main mechanisms that may bring about epochal evolution. In the first mechanism the population is imagined to evolve on a "fitness landscape" that assigns a fitness to each point in a space of genotypes. Metastability then occurs when the population gets stuck around a "local optimum" in the fitness landscape. An innovation takes place when a rare sequence of mutations creates a lineage of individuals that crosses a valley of low fitness toward a local optimum of higher fitness. In the second mechanism, the genotype space is thought to decompose into a relatively small number of "neutral subbasins": large connected sets of equal fitness genotypes. In this view, an epoch corresponds to a time period in which the fittest members of the population diffuse through a neutral subbasin under mutation until one of them discovers a (rare) connection to a neutral subbasin of higher fitness. In somewhat different methodology, in the first mechanism the metastability is caused by a "fitness barrier", whereas in the second mechanism it is caused by an "entropy barrier". In this thesis, the evolutionary dynamics in the presence of both fitness and entropy barriers is studied, although the focus is on fitness functions with entropy barriers. For a large class of simple fitness functions we derive, using the maximum entropy methodology, equations of motion on the level of fitness distributions from the underlying microscopic dynamics of selection and mutation on genotypes. In the "thermodynamic limit" of infinite population sizes the population follows these equations of motion precisely while for finite populations the population only follows these equations of motion on average at each time step. From this formulation of the finite population dynamics we determine explicitly the locations of epochs in the space of fitness distributions, the stochastic dynamics within and between the epochs, the average durations of epochs, and the stability of epochs. The results also bear directly on the dynamics of evolutionary search algorithms such as genetic algorithms. In two chapters of this thesis, our mathematical model is used to derive optimal parameter settings for evolutionary search on a wide class of fitness functions. The analysis suggests that optimal evolutionary search occurs in a parameter regime where the highest fitness strings in the population are only marginally stable. We also studied in detail the way in which an evolving population will spread through a neutral subbasin or neutral network. As the theory shows, the limit distribution of the population over a neutral network is independent of almost all evolutionary parameters and is determined solely by the topology of the neutral network. Additionally, this distribution is concentrated at those areas of the neutral network where the "neutrality" is largest. This implies that under neutral evolution the population evolves to increase its "mutational robustness" and that the mutational robustness that it attains is only dependent on the topology of the neutral network on which the population evolves. Finally, we studied and compared the barrier crossing times for fitness and entropy barriers. The results show that entropy barrier crossing in evolving population takes place on much shorter time scales than fitness barrier crossing. This suggests that in evolution, the escape from a metastable state is most likely to occur along neutral paths in genotype space

The application of artificial intelligence techniques to a sequencing problem in the biological domain

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN002816 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Optimization Framework for a Radio Frequency Gun Based Injector

Linear accelerator based light sources are used to produce coherent x-ray beams with unprecedented peak intensity. In these devices, the key parameters of the photon beam such as brilliance and coherence are directly dependent on the electron beam parameters. This leads to stringent beam quality requirements for the electron beam source. Radio frequency (RF) guns are used in such light sources since they accelerate electrons to relativistic energies over a very short distance, thus minimizing the beam quality degradation due to space charge effects within the particle bunch. Designing such sources including optimization of its beam parameters is a complex process where one needs to meet many requirements simultaneously. It is useful to have a tool to automate the design optimization in the context of the injector beam dynamics performance. Evolutionary and genetic algorithms are powerful tools to apply to nonlinear multi-objective optimization problems, and they have been successfully used in injector optimizations where the electric field profiles for the accelerating devices are fixed. Here the genetic algorithm based approach is extended to modify and optimize the electric field profile for an RF gun concurrently with the injector performance. Two field modification methods are used. This dissertation presents an overview of the optimization system and examples of its application to a state of the art RF gun. Results indicate improved injector performance is possible with unbalanced electric field profiles where the peak field in the cathode cell is larger than in subsequent cells

Unifying a Geometric Framework of Evolutionary Algorithms and Elementary Landscapes Theory

Evolutionary algorithms (EAs) are randomised general-purpose strategies, inspired by natural evolution, often used for finding (near) optimal solutions to problems in combinatorial optimisation. Over the last 50 years, many theoretical approaches in evolutionary computation have been developed to analyse the performance of EAs, design EAs or measure problem difficulty via fitness landscape analysis. An open challenge is to formally explain why a general class of EAs perform better, or worse, than others on a class of combinatorial problems across representations. However, the lack of a general unified theory of EAs and fitness landscapes, across problems and representations, makes it harder to characterise pairs of general classes of EAs and combinatorial problems where good performance can be guaranteed provably. This thesis explores a unification between a geometric framework of EAs and elementary landscapes theory, not tied to a specific representation nor problem, with complementary strengths in the analysis of population-based EAs and combinatorial landscapes. This unification organises around three essential aspects: search space structure induced by crossovers, search behaviour of population-based EAs and structure of fitness landscapes. First, this thesis builds a crossover classification to systematically compare crossovers in the geometric framework and elementary landscapes theory, revealing a shared general subclass of crossovers: geometric recombination P-structures, which covers well-known crossovers. The crossover classification is then extended to a general framework for axiomatically analysing the population behaviour induced by crossover classes on associated EAs. This shows the shared general class of all EAs using geometric recombination P-structures, but no mutation, always do the same abstract form of convex evolutionary search. Finally, this thesis characterises a class of globally convex combinatorial landscapes shared by the geometric framework and elementary landscapes theory: abstract convex elementary landscapes. It is formally explained why geometric recombination P-structure EAs expectedly can outperform random search on abstract convex elementary landscapes related to low-order graph Laplacian eigenvalues. Altogether, this thesis paves a way towards a general unified theory of EAs and combinatorial fitness landscapes

Comparison of Stochastic Global Optimization Methods: Estimating Neural Network Weights

Agricultural Economic

Application of genetic algorithm to a forced landing manoeuvre on transfer of training analysis

This study raises some issues for training pilots to fly forced landings and examines the impact that these issues may have on the design of simulators for such training. It focuses on flight trajectories that a pilot of a single-engine general aviation aircraft should fly after engine failure and how pilots can be better simulator trained for this forced landing manoeuvre. A sensitivity study on the effects of errors and an investigation on the effect of tolerances in the aerodynamic parameters as prescribed in the Manual of Criteria for the Qualification of Flight Simulators have on the performance of flight simulators used for pilot training was carried out. It uses a simplified analytical model for the Beech Bonanza model E33A aircraft and a vertical atmospheric turbulence based on the MIL-F-8785C specifications. It was found that the effect of the tolerances is highly s ensitive on the nature of the manoeuvre flown and that in some cases, negative transfer of training may be induced by the tolerances. A forced landing trajectory optimisation was carried out using Genetic Algorithm. The forced landing manoeuvre analyses with pre-selected touchdown locations and pre-selected final headings were carried out for an engine failure at 650 ft AGL for bank angles varying from banking left at 45° to banking right at 45°, and with an aircraft's speed varying from 75.6 mph to 208 mph, corresponding to 5% above airplane's stall speed and airplane's maximum speed respectively. The results show that certain pre-selected touchdown locations are more susceptible to horizontal wind. The results for the forced landing manoeuvre with a pre-selected location show minimal distance error while the quality of the results for the forced landing manoeuvre with a pre-selected location and a final heading show that the results depend on the end constraints. For certain pre-selected touchdown locations and final headings, the airplane may either touchdown very close to the pre-selected touchdown location but with greater final h eading error from the pre-selected final heading or touchdown with minimal final heading error from the pre-selected final heading but further away from the pre-selected touchdown location. Analyses for an obstacle avoidance forced landing manoeuvre were also carried out where an obstacle was intentionally placed in the flight path as found by the GA program developed for without obstacle. The methodology developed successfully found flight paths that will avoid the obstacle and touchdown near the pre-selected location. In some cases, there exist more than one ensemble grouping of flight paths. The distance error depends on both the pre-selected touchdown location and where the obstacle was placed. The distance error tends to increase with the addition of a specific final heading requirement for an obstacle avoidance forced landing manoeuvre. As with the case without specific final heading requirement, there is a trade off between touching down nearer to the pre-selected location and touching down with a smaller final heading error