Visual landmarks systems for humanoid robots

In this paper, we consider a model of visual landmarks selection for humanoid robots navigation. This model is based on a genetic programming approach

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposal

Purpose: Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.Peer Reviewe

Linear programming applied to dairy cattle selection

Paper 1 outlines a generalization to Hill\u27s equations for predicting response to selection. Equations are developed that account for multiple stage selection in either or both sexes and the flow of genes for animals selected at later stages. The asymptotic response to a single cycle of selection is shown to agree with classical selection theory. The equations applied to a dairy progeny testing scheme representative of an artificial insemination organization in the USA. The predicted asymptotic rates to a single cycle of selection were overestimated by 6% and the cumulative response to continuous selection over 20 years was overestimated by 8% when single stage male selection model was compared to two stage selection model;A linear programming model that accounts for the economic consequences of response to selection to the producer enterprise over a given planning horizon is described in Paper 2. A procedure is given in detail for defining upper lower bound constraints on variables that are correlated in the linear programming model. The optimal response to selection per year for the production traits was closest to their maximums achievable from a gene-flow model. Of all the non-production traits, days open had the greatest proportion of its maximum achievable from a gene-flow model. The linear programming model was used to compute relative economic weights (REV). The REVs for milk, fat, and protein production were considerably larger than the REVs for the non-production traits for all planning horizons. Somatic cell score had the largest REVs of the non-production traits in all planning horizons;In the third paper multiple-trait REML was used to estimate the heritabilities and the genetic and phenotypic correlations for 48- and 72-mo herd life from sire models incorporating sire relationships. Two traits were defined for 48- and 72-mo herd life, true herd life (THL) and functional herd life (FHL), which were adjusted for milk production prior to culling. The genetic correlations were used to compute weights for indirect prediction of true and functional herd-life PTA from linear-type traits PTA. (Abstract shortened by UMI.

Backward Unraveling over Time: The Evolution of Strategic Behavior in the Entry-Level British Medical Labor Markets

This paper studies an adaptive artificial agent model using a genetic algorithm to analyze how a population of decision-makers learns to coordinate on the selection of an equilibrium or a social convention in a two-sided matching game. In the contexts of centralized and decentralized entry-level labor markets, evolution and adjustment paths of unraveling are explored using this model in an environment inspired by the Kagel and Roth (Quarterly Journal of Economics, 2000) experimental study. As an interesting result, it is demonstrated that stability need not be required for the success of a matching mechanism under incomplete information in the long run.Genetic algorithms, linear programming matching, stability, two-sided matching, unraveling

Strategies in Underwriting the Costs of Catastrophic Disease

In this thesis we address the problem of integrated software pipelining for clustered VLIW architectures. The phases that are integrated and solved as one combined problem are: cluster assignment, instruction selection, scheduling, register allocation and spilling. As a first step we describe two methods for integrated code generation of basic blocks. The first method is optimal and based on integer linear programming. The second method is a heuristic based on genetic algorithms. We then extend the integer linear programming model to modulo scheduling. To the best of our knowledge this is the first time anybody has optimally solved the modulo scheduling problem for clustered architectures with instruction selection and cluster assignment integrated. We also show that optimal spilling is closely related to optimal register allocation when the register files are clustered. In fact, optimal spilling is as simple as adding an additional virtual register file representing the memory and have transfer instructions to and from this register file corresponding to stores and loads. Our algorithm for modulo scheduling iteratively considers schedules with increasing number of schedule slots. A problem with such an iterative method is that if the initiation interval is not equal to the lower bound there is no way to determine whether the found solution is optimal or not. We have proven that for a class of architectures that we call transfer free, we can set an upper bound on the schedule length. I.e., we can prove when a found modulo schedule with initiation interval larger than the lower bound is optimal. Experiments have been conducted to show the usefulness and limitations of our optimal methods. For the basic block case we compare the optimal method to the heuristic based on genetic algorithms. This work has been supported by The Swedish national graduate school in computer science (CUGS) and Vetenskapsrådet (VR)