Automatically designing more general mutation operators of evolutionary programming for groups of function classes using a hyper-heuristic

In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes

A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex

Hyper-heuristics are search methodologies which operate at a higher level of abstraction than traditional search and optimisation techniques. Rather than operating on a search space of solutions directly, a hyper-heuristic searches a space of low-level heuristics or heuristic components. An iterative selection hyper-heuristic operates on a single solution, selecting and applying a low-level heuristic at each step before deciding whether to accept the resulting solution. Crossover low-level heuristics are often included in modern selection hyper-heuristic frameworks, however as they require multiple solutions to operate, a strategy is required to manage potential solutions to use as input. In this paper we investigate the use of crossover control schemes within two existing selection hyper-heuristics and observe the difference in performance when the method for managing potential solutions for crossover is modified. Firstly, we use the crossover control scheme of AdapHH, the winner of an international competition in heuristic search, in a Modified Choice Function - All Moves selection hyper-heuristic. Secondly, we replace the crossover control scheme within AdapHH with another method taken from the literature. We observe that the performance of selection hyper-heuristics using crossover low level heuristics is not independent of the choice of strategy for managing input solutions to these operators

A modified choice function hyper-heuristic controlling unary and binary operators

Hyper-heuristics are a class of high-level search methodologies which operate on a search space of low-level heuristics or components, rather than on solutions directly. Traditional iterative selection hyper-heuristics rely on two key components, a heuristic selection method and a move acceptance criterion. Choice Function heuristic selection scores heuristics based on a combination of three measures, selecting the heuristic with the highest score. Modified Choice Function heuristic selection is a variant of the Choice Function which emphasises intensification over diversification within the heuristic search process. Previous work has shown that improved results are possible in some problem domains when using Modified Choice Function heuristic selection over the classic Choice Function, however in most of these cases crossover low-level heuristics (operators) are omitted. In this paper, we introduce crossover low-level heuristics into a Modified Choice Function selection hyper-heuristic and present results over six problem domains. It is observed that although on average there is an increase in performance when using crossover low-level heuristics, the benefit of using crossover can vary on a per-domain or per-instance basis

Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored

The Validation of a Functional, Isolated Pig Bladder Model for Physiological Experimentation

Characterizing the integrative physiology of the bladder requires whole organ preparations. The purpose of this study was to validate an isolated large animal (pig) bladder preparation, through arterial and intravesical drug administration, intravesical pressure recording, and filming of surface micromotions. Female pig bladders were obtained from the local abattoir and arterially perfused in vitro. Arterial and intravesical pressures were recorded at varying volumes. Bladder viability was assessed histologically and by monitoring inflow and outflow pH. Arterial drug administration employed boluses introduced into the perfusate. Intravesical administration involved slow instillation and a prolonged dwell-time. Surface micromotions were recorded by filming the separation of surface markers concurrently with intravesical pressure measurement. Adequate perfusion to all bladder layers was achieved for up to 8 h; there was no structural deterioration nor alteration in inflow and effluent perfusate pH. Arterial drug administration (carbachol and potassium chloride) showed consistent dose-dependent responses. Localized movements (micromotions) occurred over the bladder surface, with variable correlation with fluctuations of intravesical pressure. The isolated pig bladder is a valid approach to study integrative bladder physiology. It remains viable when perfused in vitro, responds to different routes of drug administration and provides a model to correlate movements of the bladder wall directly to variation of intravesical pressure

Recent Legal Literature

Willard: Notes to the Spanish Civil Code; Howe: Studies in the civil Law and its relations to the jurisprudence of England and America, with references to the alw of our insular possessions; Judson: The Law of Interstate Commerce and its Federal Regulation; Bigelow: The Law of Crimes; Wheeler: Daniel Webster, The Expounder of the Constitution; Goodwin: A Treatise on the Law of Real Propert

A modified indicator-based evolutionary algorithm (mIBEA)

Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations

Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored

Note and Comment

When the Descendants of a Predeceased Legatee Will Not Take Under a Statute of Substitution - There are in most states statutes declaring that if a person named as legatee dies before the testator, his descendants shall take his share. Downing v. Nicholson, 115 Ia. 493; Strong v. Smith, 84 Mich. 567; x8 A. & E. ENCYC. Ol LAw, 2d Ed. 755. A common type is such as is found in the Civil Code of California, sec. 1310, viz.: When any estate is devised or bequeathed to any child or other relation of the testator and the devisee or legatee dies before the testator, leaving lineal descendants, such descendants take the estate so given by the will in the same manner as the devisee or legatee would have done had he survived the testator. Under this statute the Supreme Court of California has just held (two judges dissenting) that descendants of a legatee dying after the will was made, but before a codicil confirming it do not take because (I) the statute is one of distribution having reference only to conditions existing at the time of death of the maker of the will and not to the time of the decease of the original legatee, (2) because the republication subsequent to the death of the legatee made the lapsed legacy a void legacy, and (3) because the statute applied only to lapsed and not to void legacies. In re Matthews\u27 Estate, (Calif. z918), x69 Pac. 233