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.

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A memetic algorithm for multi-objective dynamic location problems

Abstract This paper describes a new multiobjective interactive memetic algorithm applied to dynamic location problems. The memetic algorithm integrates genetic procedures and local search. It is able to solve capacitated and uncapacitated multi-objective single or multi-level dynamic location problems. These problems are characterized by explicitly considering the possibility of a facility being open, closed and reopen more than once during the planning horizon. It is possible to distinguish the opening and reopening periods, assigning them different coefficient values in the objective functions. The algorithm is part of an interactive procedure that asks the decision maker to define interesting search areas by establishing limits to the objective function values or by indicating reference points. The procedure will be applied to some illustrative location problems