A comparative study of the Lasso-type and heuristic model selection methods

This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remains consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. Monte-Carlo simulation results are reported to illustrate the performance of the methods.Model selection, Lasso, adaptive Lasso, elastic net, heuristic methods, genetic algorithms

Genetic Algorithms Applied To The (\u27p\u27)-median Problem

This thesis is concerned with the application of genetic algorithms to solve the p-median problem. The problem finds a specified number of locations that are the most accessible among a fixed set of locations. Genetic algorithms are an adaptive search method based on models of mathematical population genetics. Basically, the algorithms simulate the evolution of a population in an environment, where selective pressure during evolution forces the population to improve. Modification to fit a particular environment is in a sense optimization. The extended analogy has the environment as the objective function of the p-median, the population as a set of solutions, and the optimal solution as creation after evolution.;This study describes the extant methodologies for solving the problem, and concludes that because they are either limited by computation time, or to small problems, methods with reliability and/or speed are needed.;The quantitative developments in population genetics are described together with the theory of natural selection. Artificial adaptive systems have used natural systems to confirm their models. Natural selection, or reproduction in proportion to measured performance, has been equated to optimization. The mechanisms of genetic algorithms are described as a stochastic process, where knowledge about the entire population is obtained through patterned sampling.;The implementation of a genetic algorithm to solve the p-median first requires a representation that is suitable for the genetic operators that simulate the reproduction of genetic material by recombining and mutation the existing material. A representation for this problem was designed and its operability proved in two algorithms. The two algorithms used in testing are given and the parameters adjusted during experimentation are reviewed.;The genetic algorithms required significant fine tuning and the invention of a new mutation operator for the p-median. Three methods of calculation the probability of selection were tested. Scaling of the objective functions prior to selection was a substantially superior method.;Several factors are thought to be responsible for the less than robust performance of the algorithm: The selective pressures may have been incorrectly specified via the probabilities of selection, and the mapping of solutions in the representation was prone to epistasis that was exacerbated by genetic drift and resulted in suboptimal solution. (Abstract shortened with permission of author.

Parameter Setting with Dynamic Island Models

In this paper we proposed the use of a dynamic island model which aim at adapting parameter settings dynamically. Since each island corresponds to a specific parameter setting, measuring the evolution of islands populations sheds light on the optimal parameter settings efficiency throughout the search. This model can be viewed as an alternative adaptive operator selection technique for classic steady state genetic algorithms. Empirical studies provide competitive results with respect to other methods like automatic tuning tools. Moreover, this model could ease the parallelization of evolutionary algorithms and can be used in a synchronous or asynchronous way

A comparative study of adaptive mutation operators for metaheuristics

Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs

An Experimental Study of Adaptive Control for Evolutionary Algorithms

The balance of exploration versus exploitation (EvE) is a key issue on evolutionary computation. In this paper we will investigate how an adaptive controller aimed to perform Operator Selection can be used to dynamically manage the EvE balance required by the search, showing that the search strategies determined by this control paradigm lead to an improvement of solution quality found by the evolutionary algorithm

Credit Assignment in Adaptive Evolutionary Algorithms

In this paper, a new method for assigning credit to search\ud operators is presented. Starting with the principle of optimizing\ud search bias, search operators are selected based on an ability to\ud create solutions that are historically linked to future generations.\ud Using a novel framework for defining performance\ud measurements, distributing credit for performance, and the\ud statistical interpretation of this credit, a new adaptive method is\ud developed and shown to outperform a variety of adaptive and\ud non-adaptive competitors